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"content": "This paper addresses the limitations of current Multimodal Large Language Models (MLLMs) in deep logical reasoning for video understanding—such as feed-forward processing constraints (lack of self-correction), poor test-time scaling, and hallucinations. Inspired by cybernetic principles (control, ... | {
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"value": "@inproceedings{\nanonymous2025cyberv,\ntitle={CyberV: A Cybernetic Framework for Enhancing Logical Reasoning in Video Understanding},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=00F7BfXLYJ},\nnote={under review}\n}"
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"value": "Current Multimodal Large Language Models (MLLMs) may struggle with tasks requiring deep logical reasoning about video content, primarily stemming from the feed-forward processing nature, which limits their ability for self-correction and iterative refinement. To address these limitations, we propose a novel framework inspired by cybernetic principles, redesigning video MLLMs as adaptive systems capable of self-monitoring, self-correction, and dynamic resource allocation during inference. Our approach, CyberV, introduces a cybernetic loop consisting of an MLLM Inference System, a Sensor, and a Controller. Specifically, the sensor monitors MLLM forward processes. It collects intermediate interpretations, such as attention drift, then the controller determines when and how to trigger self-correction and generate feedback to guide the next round. This test-time adaptive scaling framework enhances frozen MLLMs without requiring training or additional components. Experiments demonstrate significant improvements on complex reasoning benchmarks: CyberV boosts Qwen2.5-VL-7B by 8.3% and InternVL3-8B by 5.5% on VideoMMMU, surpassing the competitive proprietary model GPT-4o. When applied to Qwen2.5-VL-72B, it yields a 10.0% improvement, achieving performance even comparable to human experts. Furthermore, on other reasoning-focused benchmarks, our method shows consistent gains of 4.6% on the multiple-choice question section of MMVU and 2.4% on MMR-V, highlighting its robustness in enhancing logical reasoning for video understanding. The code will be released to support further research."
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"value": "applications to computer vision, audio, language, and other modalities"
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"title": {
"value": "CyberV: A Cybernetic Framework for Enhancing Logical Reasoning in Video Understanding"
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2,026 | 00HNN8O7Ni | [
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"content": "This paper proposed a new reinforcement learning framework of synthesizing hardware circuits based on the feedback from model checking results.\nThe experiments are based on open datasets and the results are outperform supervised learning baselines.\n\nPros:\n1. The integration of model checking r... | {
"cdate": 1758322705432,
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"value": "We propose a deep learning approach for reactive synthesis that first initializes a model with imitation learning and then continues training by reinforcing formally verified solutions."
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"value": "@inproceedings{\nanonymous2025learning,\ntitle={Learning Reactive Synthesis from Model Checking Feedback},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=00HNN8O7Ni},\nnote={under review}\n}"
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"value": "Deep learning applications to formal verification typically fall into one of two categories: employing reinforcement learning that suffers from slow convergence, or supervised learning that suffers from limited exploration. For reactive synthesis, the problem of automatically constructing a system that satisfies a formal specification, existing approaches fall into the latter category. In this paper, we propose a hybrid approach that only initializes the model with supervised learning and then continues training by reinforcing formally verified predictions. We show that by training the model to synthesize correct solutions rather than fixating on the supervised data, performance substantially improves. We can further utilize our approach to optimize for size without any performance degradation. Finally, we show that we can iteratively reinforce on open problems that synthesis tools are unable to solve. Our approach is demonstrated for both deep neural networks trained from scratch and pre-trained models fine-tuned on reactive synthesis, establishing new state-of-the-art results for learning reactive synthesis."
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"value": "neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)"
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"title": {
"value": "Learning Reactive Synthesis from Model Checking Feedback"
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2,026 | 00UQtHqB2k | [
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"content": "The paper proposes a unified way to evaluate group fairness through sparsity. It studies links among Maximum Pairwise Difference, the Gini Index, and a PQ Index and argues that higher sparsity means lower fairness. Based on this view, it replaces the pairwise step in common criteria with a sparsit... | {
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"value": "@inproceedings{\nanonymous2025toward,\ntitle={Toward Unifying Group Fairness Evaluation from a Sparsity Perspective},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=00UQtHqB2k},\nnote={under review}\n}"
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"value": "Ensuring algorithmic fairness remains a significant challenge in machine learning, particularly as models are increasingly applied across diverse domains. While numerous fairness criteria exist, they often lack generalizability across different machine learning problems. This paper examines the connections and differences among various sparsity measures in promoting fairness and proposes a unified sparsity-based framework for evaluating algorithmic fairness. The framework aligns with existing fairness criteria and demonstrates broad applicability to a wide range of machine learning tasks. We demonstrate the effectiveness of the proposed framework as an evaluation metric through extensive experiments on a variety of datasets and bias mitigation methods. This work provides a novel perspective to algorithmic fairness by framing it through the lens of sparsity and social equity, offering potential for broader impact on fairness research and applications."
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"value": "ICLR 2026 Conference Submission"
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2,026 | 017F77AYeQ | [
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"content": "The paper proposes SMART-3D, a mask token modeling approach for 3D generation.",
"id": "gZowcvNNqh",
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"content": "The paper proposes an framework that merges masked autoregressive generation with diffusion modeling and linear attention, addressing key efficiency bot... | {
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"_bibtex": {
"value": "@inproceedings{\nanonymous2025smartd,\ntitle={{SMART}-3D: Scaling Masked AutoRegressive Transformer for Efficient 3D Shape Generation},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=017F77AYeQ},\nnote={under review}\n}"
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"value": "Autoregressive models have shown promise in 3D shape generation by modeling complex spatial dependencies between discrete shape tokens. However, their sequential nature and token-by-token sampling limit scalability and generation speed, especially for high-resolution shapes. In this work, we propose SMART-3D (Scaling Masked AutoRegressive Transformers for 3D generation), a novel framework that combines the modeling capacity of autoregressive transformers with the efficiency of masked generation. By introducing a hierarchical token representation and a progressive masked generation schedule, SMART-3D enables parallel decoding of 3D structures without sacrificing autoregressive fidelity. We further optimize the model with spatially-aware masking and lightweight transformer blocks, allowing generation of detailed 3D shapes with significantly reduced computational overhead. Experiments on ShapeNet, ModelNet, and ShapeNet-55 datasets demonstrate that SMART-3D achieves state-of-the-art performance in both generation quality and speed, outperforming previous competitive baselines. Our approach offers a scalable and practical solution for high-fidelity 3D shape synthesis in real-world applications."
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"title": {
"value": "SMART-3D: Scaling Masked AutoRegressive Transformer for Efficient 3D Shape Generation"
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"value": "ICLR 2026 Conference Submission"
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2,026 | 023yMrtHQP | [
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"content": "This paper introduces a prompting framework, named Expectation–Evidence Prompting (EEP), for large language models to enhance factual verification. Drawing from the Strategic Use of Evidence technique in cognitive psychology, EEP involves generating two sets of expectations, supportive and refutat... | {
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"_bibtex": {
"value": "@inproceedings{\nanonymous2025expectationevidence,\ntitle={Expectation{\\textendash}Evidence Prompting: Structuring Verification by Comparing Expected and Observed Evidence},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=023yMrtHQP},\nnote={under review}\n}"
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"abstract": {
"value": "Large language models (LLMs) often fail in factual verification due to hallucinations, unreliable truthfulness judgments, and opaque reasoning. We identify a structural limitation underlying these failures: LLMs directly compare claims with evidence without accounting for expected refutational alternatives. Specifically, we demonstrate that this omission leads to ambiguity in contradiction detection and unreliable abstention. Leveraging this observation, we introduce Expectation-Evidence Prompting (EEP), a cognitively inspired strategy that first generates supportive and refutational expectations from a claim and then aligns them with observed evidence. This bidirectional reasoning process enforces logical symmetry, reduces bias toward agreement, and provides a principled abstention mechanism. Across three fact-checking benchmarks: FEVER, PubHealth, and SciFact, EEP achieves consistent gains over strong prompting baselines, including an 86.3 macro-F1 on FEVER (+3.6 over Chain-of-Thought), 82.1 precision on PubHealth (highest among all methods), and 76.1 F1 on the Supports class in SciFact. These results demonstrate that embedding expectation evidence alignment into prompt design yields more interpretable, robust, and trustworthy factual reasoning in LLMs."
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2,026 | 02NbD16OnA | [
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"content": "This paper introduces DECEPTIONDECODED, a multimodal news benchmark with explicitly defined creator intent to support misleading intent detection, source attribution, and desire inference. It reveals that current VLMs fail to reason about intent beyond surface alignment and stylistic cues.",
"... | {
"cdate": 1756910313383,
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"value": "We reveal that state-of-the-art VLMs remain blind to misleading creator intent, establishing the need for intent-aware benchmarks and models as the next frontier in multimodal misinformation detection."
},
"_bibtex": {
"value": "@inproceedings{\nanonymous2025seeing,\ntitle={Seeing Through Deception: Uncovering Misleading Creator Intent in Multimodal News with Vision-Language Models},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=02NbD16OnA},\nnote={under review}\n}"
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"value": "The impact of misinformation arises not only from factual inaccuracies but also from the misleading narratives that creators deliberately embed. Interpreting such creator intent is therefore essential for multimodal misinformation detection (MMD) and effective information governance. To this end, we introduce DeceptionDecoded, a large-scale benchmark of 12,000 image–caption pairs grounded in trustworthy reference articles, created using an intent-guided simulation framework that models both the desired influence and the execution plan of news creators. The dataset captures both misleading and non-misleading cases, spanning manipulations across visual and textual modalities, and supports three intent-centric tasks: (1) misleading intent detection, (2) misleading source attribution, and (3) creator desire inference. We evaluate 14 state-of-the-art vision–language models (VLMs) and find that they struggle with intent reasoning, often relying on shallow cues such as surface-level alignment, stylistic polish, or heuristic authenticity signals. These results highlight the limitations of current VLMs and position DeceptionDecoded as a foundation for developing intent-aware models that go beyond shallow cues in MMD."
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2,026 | 02cEkpURXH | [
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"content": "This paper proposes a KD–based training strategy for OOD generalization. The authors first argue that training compact student models via simple KD from a teacher with strong OOD performance can often surpass standalone algorithmic DG methods. They further note that prior OOD-oriented KD approache... | {
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"value": "@inproceedings{\nanonymous2025early,\ntitle={Early Layer Readouts for Robust Knowledge Distillation},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=02cEkpURXH},\nnote={under review}\n}"
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"value": "Domain generalization (DG) aims to learn a model that can generalize to unseen i.e. out-of-distribution (OOD) test domain. While large-capacity networks trained with sophisticated DG algorithms tend to achieve high robustness, they tend to be impractical in deployment. Typically, Knowledge distillation (KD) can alleviate this via an efficient transfer of knowledge from a robust teacher to a smaller student network. Throughout our experiments, we find that vanilla KD already provides strong OOD performance, often outperforming standalone DG algorithms. Motivated by this observation, we propose an adaptive distillation strategy that utilizes early layer predictions and uncertainty measures to learn a meta network that effectively rebalances supervised and distillation losses as per sample difficulty. Our method adds no inference overhead and consistently outperforms canonical ERM, vanilla KD, and competing DG algorithms across OOD generalization benchmarks."
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2,026 | 02mBAZjFzp | [
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"content": "This paper introduces VRPAGENT, a framework for discovering heuristic operators for Vehicle Routing Problems (VRPs) using large language models (LLMs). The method combines LLM-generated “destroy” and “order” operators with a Large Neighborhood Search (LNS) metaheuristic, leveraging genetic algorit... | {
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"value": "We introduce VRPAgent, a framework that leverages LLMs and evolutionary search to discover novel heuristic operators for vehicle routing problems, achieving state-of-the-art performance across multiple VRP variants."
},
"_bibtex": {
"value": "@inproceedings{\nanonymous2025vrpagent,\ntitle={{VRPA}gent: {LLM}-Driven Discovery of Heuristic Operators for Vehicle Routing Problems},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=02mBAZjFzp},\nnote={under review}\n}"
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"value": "Designing high-performing heuristics for vehicle routing problems (VRPs) is a complex task that requires both intuition and deep domain knowledge. Large language model (LLM)-based code generation has recently shown promise across many domains, but it still falls short of producing heuristics that rival those crafted by human experts. In this paper, we propose VRPAgent, a framework that integrates LLM-generated components into a metaheuristic and refines them through a novel genetic search. By using the LLM to generate problem-specific operators, embedded within a generic metaheuristic framework, VRPAgent keeps tasks manageable, guarantees correctness, and still enables the discovery of novel and powerful strategies. Across multiple problems, including the capacitated VRP, the VRP with time windows, and the prize-collecting VRP, our method discovers heuristic operators that outperform handcrafted methods and recent learning-based approaches while requiring only a single CPU core. To our knowledge, VRPAgent is the first LLM-based paradigm to advance the state-of-the-art in VRPs, highlighting a promising future for automated heuristics discovery."
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2,026 | 02mgFnnfqG | [
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"content": "The paper presents LiveMoments, a method for selecting and restoring a new low-quality (LQ) key photo from a short clip surrounding some key high-quality (HQ) photo. To this end, the authors build a model based on latent flow models and learnable networks for the HQ key image, the LQ candidate, an... | {
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"TLDR": {
"value": "We are the first to restore reselected key photos in Live Photos, achieving perceptual fidelity beyond existing solutions in real-world scenes."
},
"_bibtex": {
"value": "@inproceedings{\nanonymous2025livemoments,\ntitle={LiveMoments: Reselected Key Photo Restoration in Live Photos via Reference-guided Diffusion},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=02mgFnnfqG},\nnote={under review}\n}"
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"value": "Live Photo captures both a high-quality key photo and a short video clip to preserve the precious dynamics around the captured moment. \nWhile users may choose alternative frames as the key photo to capture better expressions or timing, these frames often exhibit noticeable quality degradation, as the photo capture ISP pipeline delivers significantly higher image quality than the video pipeline. This quality gap highlights the need for dedicated restoration techniques to enhance the reselected key photo. To this end, we propose LiveMoments, a reference-guided image restoration framework tailored for the reselected key photo in Live Photos. Our method employs a two-branch neural network: a reference branch that extracts structural and textural information from the original high-quality key photo, and a main branch that restores the reselected frame using the guidance provided by the reference branch. Furthermore, we introduce a unified Motion Alignment module that incorporates motion guidance for spatial alignment at both the latent and image levels. Experiments on real and synthetic Live Photos demonstrate that LiveMoments significantly improves perceptual quality and fidelity over existing solutions, especially in scenes with fast motion or complex structures."
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"title": {
"value": "LiveMoments: Reselected Key Photo Restoration in Live Photos via Reference-guided Diffusion"
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"value": "ICLR 2026 Conference Submission"
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2,026 | 032sg6mGp9 | [
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{
"content": "This paper introduces a multinomial mixture modelling approach to address the identifiability problem in learning from noisy labels (LNL). The authors theoretically prove that LNL becomes identifiable when each sample has at least 2C−1 independent noisy labels, enabling the unique recovery of clea... | {
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"_bibtex": {
"value": "@inproceedings{\nanonymous2025identifiability,\ntitle={Identifiability in Noisy Label Learning: A Multinomial Mixture Modelling Approach},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=032sg6mGp9},\nnote={under review}\n}"
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"value": "Learning from noisy labels (LNL) is crucial in deep learning, in which one of the approaches is to identify clean-label samples from poorly-annotated datasets. Such an identification is challenging because the conventional LNL problem, which assumes only one noisy label per instance, is non-identifiable, i.e., clean labels cannot be estimated theoretically without additional heuristics. This paper presents a novel data-driven approach that addresses this issue without requiring any heuristics about clean samples. We discover that the LNL problem becomes identifiable if there are at least $2C - 1$ i.i.d. noisy labels per instance, where $C$ is the number of classes. Our finding relies on the assumption of i.i.d. noisy labels and multinomial mixture modelling, making it easier to interpret than previous studies that require full-rank noisy-label transition matrices. To fulfil this condition without additional manual annotations, we propose a method that automatically generates additional i.i.d. noisy labels through nearest neighbours. These noisy labels are then used in the Expectation-Maximisation algorithm to infer clean labels. Our method demonstrably estimates clean labels accurately across various label noise benchmarks, including synthetic, web-controlled, and real-world datasets. Furthermore, the model trained with our method performs competitively with many state-of-the-art methods."
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"title": {
"value": "Identifiability in Noisy Label Learning: A Multinomial Mixture Modelling Approach"
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2,026 | 03Ek1qDZmI | [
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"content": "This paper introduces SSTP, a sample selection framework for trajectory prediction. The primary motivation is to address two challenges in existing large-scale datasets: the high computational cost of training and the imbalance where common, low-density scenarios dominate over rare, safety-critica... | {
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"_bibtex": {
"value": "@misc{\nyang2025sstp,\ntitle={{SSTP}: Efficient Sample Selection for Trajectory Prediction},\nauthor={Ruining Yang and Yi Xu and Yun Fu and Lili Su},\nyear={2025},\nurl={https://openreview.net/forum?id=03Ek1qDZmI}\n}"
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"value": "Trajectory prediction is a core task in autonomous driving. However, training advanced trajectory prediction models on existing large-scale datasets is both time-consuming and computationally expensive. More critically, these datasets are highly imbalanced in scenario density, with normal driving scenes (low-moderate traffic) overwhelmingly dominating the datasets, while high-density and safety-critical cases are underrepresented. As a result, models tend to overfit low/moderate-density scenarios and perform poorly in high-density scenarios. To address these challenges, we propose the SSTP framework, which constructs a compact yet density-balanced dataset tailored to trajectory prediction. SSTP consists of two main stages: (1) Extraction, where a baseline model is pretrained for a few epochs to obtain stable gradient estimates, and the dataset is partitioned by scenario density. (2) Selection, where gradient-based scores and a submodular objective select representative samples within each density category, while biased sampling emphasizes rare high-density interactions to avoid dominance by low-density cases. This approach significantly reduces the dataset size and mitigates scenario imbalance, without sacrificing prediction accuracy. Experiments on the Argoverse 1 and Argoverse 2 datasets with recent state-of-the-art models show that SSTP achieves comparable performance to full-dataset training using only half the data while delivering substantial improvements in high-density traffic scenes and significantly reducing training time. Robust trajectory prediction depends not only on data scale but also on balancing scene density to ensure reliable performance under complex multi agent interactions. The code is available at https://anonymous.4open.science/r/SSTP_v2-69E5/README.md."
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"value": "applications to robotics, autonomy, planning"
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"title": {
"value": "SSTP: Efficient Sample Selection for Trajectory Prediction"
},
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"value": "ICLR 2026 Conference Withdrawn Submission"
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2,026 | 03MfCNn3pF | [
2,
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"content": "This paper presents PersonalQ, a two-stage system for personalized diffusion model serving. Check-in selects the intended personalized checkpoint via metadata reasoning and LLM-based prompt clarification, while Trigger-Aware Quantization (TAQ) preserves trigger-token features during quantization t... | {
"cdate": 1757994763056,
"content": {
"TLDR": {
"value": "PersonalQ enables efficient serving of personalized diffusion models at scale through intelligent checkpoint selection and trigger-token-aware quantization that preserves personalization quality while reducing memory footprint."
},
"_bibtex": {
"value": "@inproceedings{\nanonymous2025personalq,\ntitle={PersonalQ: Select, Quantize, and Serve Personalized Diffusion Models for Efficient Inference},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=03MfCNn3pF},\nnote={under review}\n}"
},
"abstract": {
"value": "Personalized text-to-image generation enables users to create custom AI models that generate their unique concepts—specific objects or artistic styles—achieving unprecedented creative control. However, deploying a large repository of personalized checkpoints faces two critical challenges: (1) ambiguous user prompts make it difficult to match the intended checkpoint in large repositories, and (2) standard post-training quantization methods degrade personalized diffusion checkpoints’ image quality. We analyze the importance of reasoning over checkpoint metadata and clarifying user prompts for intent-aligned checkpoint selection. Additionally, we find that trigger tokens for personalized diffusion play a crucial role in quantization. To address the challenges, we propose PersonalQ, a unified system with two components: Check-in analyzes checkpoint repositories and clarifies user intent for intent-aligned selection, and TAQ (Trigger-Aware Quantization), which protects the trigger-token-related representation to deliver high-quality inference from the chosen checkpoint under quantization. On our Repo-Prompts benchmark, PersonalQ achieves an 89% checkpoint-selection preference win rate and a 4.42/5 intent score. Across benchmarks, TAQ reduces inference memory by up to 75% while maintaining strong text-image alignment (CLIP score 0.297 vs. 0.315 at full precision) and image fidelity (FID 11.03 at W8A8 vs. 10.96 at full precision), enabling scalable deployment of personalized models without compromising quality."
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"title": {
"value": "PersonalQ: Select, Quantize, and Serve Personalized Diffusion Models for Efficient Inference"
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"value": "ICLR 2026 Conference Submission"
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2,026 | 03QzvMzxVM | [
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"content": "This work presents Robust-NLL, which serves as a plug-and-play loss replacing vanilla NLL loss for robust uncertainty-aware training against label-space outliers. The proposed loss function uses softmax reweighting over sample losses to filter out outliers. The author also provides theoretical ana... | {
"cdate": 1758019401870,
"content": {
"TLDR": {
"value": "We introduce Robust-NLL for modeling uncertainty under the presence of outliers."
},
"_bibtex": {
"value": "@inproceedings{\nanonymous2025robust,\ntitle={Robust Uncertainty-Aware Learning via Boltzmann-weighted {NLL}},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=03QzvMzxVM},\nnote={under review}\n}"
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"abstract": {
"value": "Uncertainty estimation is critical for deploying deep learning models in high-stakes applications such as autonomy and decision-making. While prior works on data uncertainty modeling estimate aleatoric uncertainty by minimizing the negative log-likelihood (NLL) loss, they often fail under the presence of outliers. To address this limitation, we introduce Robust-NLL, a drop-in replacement for vanilla NLL that filters noisy or adversarial samples. Robust-NLL learns robust uncertainty estimates in neural networks through a Boltzmann-weighted NLL loss that requires no architectural changes, additional parameters, or iterative procedures, and acts as a plug-and-play loss function that maintains full differentiability and mini-batch compatibility. We evaluate our approach on synthetic regression tasks and real-world visual localization benchmarks with injected outliers. Experimental results demonstrate that simply replacing NLL with Robust-NLL consistently improves both prediction accuracy and reliability of uncertainty estimates, achieving substantial performance gains across diverse tasks and architectures."
},
"anonymous_url": null,
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"value": "probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)"
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"title": {
"value": "Robust Uncertainty-Aware Learning via Boltzmann-weighted NLL"
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"venue": {
"value": "ICLR 2026 Conference Submission"
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"value": "ICLR.cc/2026/Conference/Submission"
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2,026 | 03ccrSpjOx | [
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"content": "The paper studies how deliberation format shapes value expression and consensus in LLM-LLM debates over everyday moral dilemmas. Using 1,000 AITA cases, the authors run pairwise and three-way debates among GPT-4.1, Claude 3.7 Sonnet, and Gemini 2.0 Flash in two settings: synchronous (parallel) and... | {
"cdate": 1758148909076,
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"_bibtex": {
"value": "@inproceedings{\nanonymous2025deliberative,\ntitle={Deliberative Dynamics and Value Alignment in {LLM} Debates},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=03ccrSpjOx},\nnote={under review}\n}"
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"abstract": {
"value": "As large language models (LLMs) are increasingly deployed in sensitive everyday contexts -- offering personal advice, mental health support, and moral guidance -- understanding their elicited values in navigating complex moral reasoning is essential. Most evaluations study this sociotechnical alignment through single-turn prompts, but it is unclear if these findings extend to multi-turn settings where values emerge through dialogue, revision, and consensus. We address this gap using LLM debate to examine deliberative dynamics and value alignment in multi-turn settings by prompting subsets of three models (GPT-4.1, Claude 3.7 Sonnet, and Gemini 2.0 Flash) to collectively assign blame in 1,000 everyday dilemmas from Reddit's \"Am I the Asshole\" community. We use both synchronous (parallel responses) and round-robin (sequential responses) formats to test order effects and verdict revision. Our findings show striking behavioral differences. In the synchronous setting, GPT showed strong inertia (0.6-3.1% revision rates) while Claude and Gemini were far more flexible (28-41%). Value patterns also diverged: GPT emphasized personal autonomy and direct communication, while Claude and Gemini prioritized empathetic dialogue. Certain values proved especially effective at driving verdict changes. We further find that deliberation format had a strong impact on model behavior: GPT and Gemini stood out as highly conforming relative to Claude, with their verdict behavior strongly shaped by order effects. These results show how deliberation format and model-specific behaviors shape moral reasoning in multi-turn interactions, underscoring that sociotechnical alignment depends on how systems structure dialogue as much as on their outputs."
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"title": {
"value": "Deliberative Dynamics and Value Alignment in LLM Debates"
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"venue": {
"value": "ICLR 2026 Conference Submission"
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2,026 | 03fFxN6Orj | [
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"content": "This paper proposed the Adviser-Actor-Critic (AAC) framework, targeting steady-state error reduction for high-precision robotic control tasks in reinforcement learning. AAC augments standard actor-critic architectures with an additional “adviser” module, implemented as a PI controller, that genera... | {
"cdate": 1758271601146,
"content": {
"TLDR": {
"value": "Adviser-Actor-Critic (AAC) combines reinforcement learning with a novel adviser to generate virtual goals, effectively reducing steady-state errors by over 80% in high-precision robotic control tasks."
},
"_bibtex": {
"value": "@misc{\nchen2025adviseractorcritic,\ntitle={Adviser-Actor-Critic: Reducing Steady-State Error in Reinforcement Learning for Robotics Control},\nauthor={Donghe Chen and Jiaxuan Yue and Yubin Peng and Tengjie Zheng and Han Wang and Chaoran Qu and Lin Cheng},\nyear={2025},\nurl={https://openreview.net/forum?id=03fFxN6Orj}\n}"
},
"abstract": {
"value": "High-precision control tasks present substantial challenges for reinforcement learning (RL) algorithms, frequently resulting in suboptimal performance attributed to network approximation inaccuracies and inadequate sample quality. While existing RL frameworks can achieve task completion at coarse precision levels, steady-state tracking errors remain a critical limitation that prevents achieving sub-hardware-level precision. We introduce Adviser-Actor-Critic (AAC), designed to address this precision control dilemma by combining the precision of feedback control theory with the adaptive learning capability of RL and featuring an Adviser that mentors the actor to refine control actions, thereby enhancing the precision of goal attainment. Through extensive benchmark environments from gymnasium-robotics, coupled with real-world quadcopter attitude control, AAC significantly outperforms standard RL algorithms in precision-critical tasks while demonstrating an average $>80\\%$ steady-state error reduction compared to baseline methods."
},
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"authors": {
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},
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"title": {
"value": "Adviser-Actor-Critic: Reducing Steady-State Error in Reinforcement Learning for Robotics Control"
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"venue": {
"value": "ICLR 2026 Conference Withdrawn Submission"
},
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2,026 | 03jzVlLxEe | [
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"content": "The authors propose **NERVE**, a noise- and variability-robust EEG foundation model designed to address key challenges in EEG analysis, including low signal-to-noise ratios (SNR), high inter-sample variability, and spatial dependencies arising from electrode placement in acquisition systems. The p... | {
"cdate": 1758337883115,
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"TLDR": null,
"_bibtex": {
"value": "@inproceedings{\nanonymous2025nerve,\ntitle={{NERVE}: Noise-Variability-Robust {EEG} Foundation Model with Electrode-Brain Interactions},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=03jzVlLxEe},\nnote={under review}\n}"
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"abstract": {
"value": "Electroencephalography (EEG) is an indispensable modality for measuring and recording brain electrical activity, with broad applications in brain–computer interfaces (BCI) and healthcare. While early EEG models predominantly adopted supervised learning methods due to the scarcity of large-scale datasets and the heterogeneity across tasks and datasets, the recent success of large foundation models has driven increasing efforts to build EEG foundation models. However, most existing studies focus on handling signals with varying formats while overlooking inherent characteristics of EEG signals during acquisition, including low signal-to-noise ratios (SNR), high variability across samples, and spatial dependencies arising from electrode placement within the acquisition system. To address these challenges, we propose NERVE, a novel noise-variability-robust EEG foundation model with electrode-brain interactions. Specifically, pre-training of NERVE begins with learning a noise-robust neural tokenizer that encodes EEG patches into discrete neural tokens. The tokenizer is trained through denoising temporal–spectral prediction to reconstruct temporal and frequency information of the original signal from noise-augmented inputs. NERVE is further pretrained to predict the neural codes of masked EEG patches, integrated with a variability-robust objective that promotes uniform EEG representations. To incorporate spatial structure in EEG, we propose an electrode-position-aware transformer as the backbone for both the tokenizer and the foundation model. It enables the model to capture spatial dependencies among electrodes and brain regions via attention mechanisms. NERVE demonstrates competitive performance across diverse BCI tasks and improved robustness to noise and variability compared to existing EEG foundation models."
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"title": {
"value": "NERVE: Noise-Variability-Robust EEG Foundation Model with Electrode-Brain Interactions"
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"value": "ICLR 2026 Conference Submission"
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2,026 | 03qTI3NKqi | [
4,
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{
"content": "This work found that previous soft prompts often disrupted information flow and reduced reasoning. They argue that soft prompts should not be limited to the activation and guidance stages but should be inserted into appropriate stages to ensure smooth information flow between layers. Therefore, th... | {
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"value": "@inproceedings{\nanonymous2025unlocking,\ntitle={Unlocking Coherent Reasoning in {LLM}s with Hierarchical Soft Prompts},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=03qTI3NKqi},\nnote={under review}\n}"
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"value": "Large language models (LLMs) exhibit strong reasoning capabilities in complex tasks. Soft prompt tuning, as a lightweight approach, injects trainable vectors into the input to guide the reasoning process and enhance model performance. Prior studies show that soft prompts effectively activate prior knowledge and improve problem understanding in the early stages of reasoning. However, when they continue to exert strong influence in the middle and later stages, they often disrupt the information flow and degrade reasoning performance. Based on this observation, we argue that the role of soft prompts should not be confined to a single stage of activation and guidance. Instead, they should be inserted at appropriate stages to ensure smooth information transmission across layers. Existing methods, however, typically rely on one-shot static injection and cannot dynamically regulate prompts across stages, leading to functional mismatches during reasoning. To address this limitation, we propose a dynamic hierarchy-aware mechanism(DHAM). This mechanism first employs hierarchical clustering to derive stage-specific representations, and then leverages the semantic guidance capability of soft prompts to adaptively align and activate them, ensuring effective coordination across reasoning stages. \nDHAM yields consistent gains across models and benchmarks (e.g., 29.5\\%→43.8\\% on Llama-2-13B/GSM8K), with ablations showing CKA clustering and moderate stage numbers (e.g., $G=3/4$) perform best, consistent with the stable information flow hypothesis."
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2,026 | 03u504EDJp | [
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"content": "This paper introduces APO, a new framework for distilling reasoning capabilities from multiple MLLMs that exhibit conceptual drift, defined as variability in their reasoning behaviors or conclusions. The core idea is that APO aggregates all available reasoning trajectories and learns to prefer the... | {
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"_bibtex": {
"value": "@inproceedings{\nanonymous2025learning,\ntitle={Learning from All: Concept Alignment for Autonomous Distillation from Multiple Drifting {MLLM}s},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=03u504EDJp},\nnote={under review}\n}"
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"value": "This paper identifies a critical yet underexplored challenge in distilling from multi-modal large language models (MLLMs): the reasoning trajectories generated by multiple drifting teachers exhibit concept drift, whereby their reasoning distributions evolve unpredictably and transmit biases to the student model, ultimately compromising its performance. To tackle this issue, we pioneer a theoretical connection between concept drift and knowledge distillation, casting the non-stationary reasoning dynamics from multiple MLLM teachers as next-token prediction of multi-stream reasoning trajectories. Guided by concept drift, we introduce the “learn–compare–critique” paradigm, culminating in autonomous preference optimization (APO). Under the active guidance of the teachers, the student model first learns and self-distils preferred thinking by comparing multiple teachers. It then engages in critical reflection over the drifting inference from teachers, performing concept alignment through APO, ultimately yielding a robust, consistent, and generalizable model. Extensive experiments demonstrate our superior performance of consistency, robustness and generalization within knowledge distillation. Besides, we also contributed a large-scale dataset CXR-MAX (Multi-teachers Alignment X-rays), comprising 170,982 distilled reasoning trajectories derived from publicly accessible MLLMs based on MIMIC-CXR. Our code and data are public at: https://anonymous.4open.science/r/Autonomous-Distillation/."
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2,026 | 040ClRXMf3 | [
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"content": "This paper proposes a new algorithm to extract cardinal-minimal sufficient explanations for Neural Additive Models (NAMs).\nIt does so by exploiting key design choices of NAMs, showing how this family of models supports explanations with guarantees.\n\nThis is achieved as follows. First, the paper... | {
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"value": "Our approach constructs provably sufficient and (globally) cardinal-minimal explanations for neural additive models with improved runtime complexity."
},
"_bibtex": {
"value": "@inproceedings{\nanonymous2025provably,\ntitle={Provably Explaining Neural Additive Models},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=040ClRXMf3},\nnote={under review}\n}"
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"abstract": {
"value": "Despite significant progress in post-hoc explanation methods for neural networks, many remain heuristic and lack provable guarantees. A key approach for obtaining explanations with provable guarantees is by identifying a *(globally) cardinal-minimal* subset of input features which by itself is *provably sufficient* to determine the prediction. However, for standard neural networks, this task is often computationally infeasible, as it demands a worst-case *exponential* number of verification queries in the number of input features, each of which is NP-hard. In this work, we show that for Neural Additive Models (NAMs), a recent and more interpretable neural network family, we can *efficiently* generate explanations with such guarantees. We present a new model-specific algorithm for NAMs that generates provably (globally) cardinal-minimal explanations using only a *logarithmic* number of verification queries in the number of input features, after a parallelized preprocessing step with logarithmic runtime in the required precision is applied to each small univariate NAM component. Our algorithm not only makes the task of obtaining (globally) cardinal minimal explanations feasible, but even outperforms existing algorithms designed to find *(locally) subset-minimal* explanations -- which may be larger and less informative but easier to compute -- despite our algorithm solving a much more difficult task. Our experiments demonstrate that, compared to previous algorithms, our approach provides provably smaller explanations than existing works and substantially reduces the computation time. Moreover, we show that our generated provable explanations offer benefits that are unattainable by standard sampling-based techniques typically used to interpret NAMs."
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"title": {
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"value": "ICLR 2026 Conference Submission"
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2,026 | 04HwYGgp2w | [
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6,
6
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{
"content": "In this paper,the authors introduces ImageDoctor, a unified,multi-aspect evaluation framework for Text-to Image(T2I) models. Unlike previous methods that provide a single scalar, ImageDoctor assesses image quality across four dimensions: plausibility, semantic alignment, aesthetics, and overall qu... | {
"cdate": 1757544654492,
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"_bibtex": {
"value": "@inproceedings{\nanonymous2025imagedoctor,\ntitle={ImageDoctor: Diagnosing Text-to-Image Generation via Grounded Image Reasoning},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=04HwYGgp2w},\nnote={under review}\n}"
},
"abstract": {
"value": "The rapid advancement of text-to-image (T2I) models has increased the need for reliable human preference modeling, a demand further amplified by recent progress in reinforcement learning for preference alignment. However, existing approaches typically quantify the quality of a generated image using a single scalar, limiting their ability to provide comprehensive and interpretable feedback on image quality. To address this, we introduce ImageDoctor, a unified multi-aspect T2I model evaluation framework that assesses image quality across four complementary dimensions: plausibility, semantic alignment, aesthetics, and overall quality. ImageDoctor also provides pixel-level flaw indicators in the form of heatmaps, which highlight misaligned or implausible regions, and can be used as a dense reward for T2I model preference alignment. Inspired by the diagnostic process, we improve the detail sensitivity and reasoning capability of ImageDoctor by introducing a ``look-think-predict\" paradigm, where the model first localizes potential flaws, then generates reasoning, and finally concludes the evaluation with quantitative scores. Built on top of a vision-language model and trained through a combination of supervised fine-tuning and reinforcement learning, ImageDoctor demonstrates strong alignment with human preference across multiple datasets, establishing its effectiveness as an evaluation metric. Furthermore, when used as a reward model for preference tuning, ImageDoctor significantly improves generation quality—achieving an improvement of 10% over scalar-based reward models."
},
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"title": {
"value": "ImageDoctor: Diagnosing Text-to-Image Generation via Grounded Image Reasoning"
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"value": "ICLR 2026 Conference Submission"
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2,026 | 04JkPDiCnp | [
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{
"content": "This paper introduces InternAgent-DR, a multi-agent deep-research framework that models scientific reasoning as a dynamic structured knowledge flow. Instead of relying on a linear task sequence, InternAgent-DR represents research workflows as directed acyclic graphs whose nodes correspond to subta... | {
"cdate": 1756820032542,
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"_bibtex": {
"value": "@inproceedings{\nanonymous2025internagentdr,\ntitle={InternAgent-{DR}: Advancing deep research with dynamic structured knowledge flow},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=04JkPDiCnp},\nnote={under review}\n}"
},
"abstract": {
"value": "Deep research is an inherently challenging task that demands both breadth and depth of thinking. It involves navigating diverse knowledge spaces and reasoning over complex, multi-step dependencies, which presents substantial challenges for agentic systems. To address this, we propose InternAgent-DR (Deep Research), a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning. InternAgent-DR is capable of strategically planning and expanding the knowledge flow to enable parallel exploration and hierarchical task decomposition, while also adjusting the knowledge flow in real time based on feedback from intermediate reasoning outcomes and insights. InternAgent-DR achieves state-of-the-art performance on both general and scientific benchmarks, including GAIA, HLE, GPQA and TRQA, demonstrating its effectiveness in multi-disciplinary research scenarios and its potential to advance scientific discovery."
},
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"title": {
"value": "InternAgent-DR: Advancing deep research with dynamic structured knowledge flow"
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"venue": {
"value": "ICLR 2026 Conference Submission"
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2,026 | 04Tfwy3LLC | [
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"content": "The paper relates to the pruning of LLM layers. The paper consists of three main parts:\n1. Discussion of criteria for identifying prunable layers\n2. Comparison between LoRA and partial fine-tuning methods for recovering accuracy after pruning\n3. Theoretical analysis of gradient flow in the pres... | {
"cdate": 1757254648198,
"content": {
"TLDR": {
"value": "This paper presents a theoretical and empirical analysis of layer pruning in Large Language Models, aiming to improve and refine pruning strategies."
},
"_bibtex": {
"value": "@inproceedings{\nanonymous2025reassessing,\ntitle={Reassessing Layer Pruning in {LLM}s: New Insights and Methods},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=04Tfwy3LLC},\nnote={under review}\n}"
},
"abstract": {
"value": "Although large language models (LLMs) have achieved remarkable success across various domains, their considerable scale necessitates substantial computational resources, posing significant challenges for deployment in resource-constrained environments. Layer pruning, as a simple yet effective compression method, removes layers of a model directly, reducing computational overhead. However, what are the best practices for layer pruning in LLMs? Are sophisticated layer selection metrics truly effective? Does the LoRA (Low-Rank Approximation) family, widely regarded as a leading method for pruned model fine-tuning, truly meet expectations when applied to post-pruning fine-tuning? To answer these questions, we dedicate thousands of GPU hours to benchmarking layer pruning in LLMs and gaining insights across multiple dimensions. Our results demonstrate that a simple approach, i.e., pruning the final layers followed by fine-tuning the lm\\_head and the remaining last three layers, yields remarkably strong performance. These pruning strategies are further supported by theoretical analyses based on the gradient flow. Following this guide, our method surpasses existing state-of-the-art pruning methods by $5.62\\%$–$17.27\\%$ on Llama-3.1-8B-It, by $2.36\\%$–$19.45\\%$ on Llama-3-8B and by $4.34\\%$–$9.59\\%$ on Llama-3-70B. The code is available on GitHub."
},
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"title": {
"value": "Reassessing Layer Pruning in LLMs: New Insights and Methods"
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"value": "ICLR 2026 Conference Submission"
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2,026 | 04h40hEgTj | [
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{
"content": "In this paper, the authors aimed at creating a family of toy models for exploring the known challenge of long-context learning for LLM. The proposed toy model have different time series data interleaved with distinct labels. The authors found that LLM developed two distinct learning mechanisms in ... | {
"cdate": 1758340263445,
"content": {
"TLDR": {
"value": "We introduce a new family of toy problems that combine features of linear-regression-style continuous in-context learning (ICL) with discrete associative recall and find distinct learning dynamics for different prediction mechanisms."
},
"_bibtex": {
"value": "@inproceedings{\nanonymous2025decomposing,\ntitle={Decomposing Prediction Mechanisms for In-context Recall},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=04h40hEgTj},\nnote={under review}\n}"
},
"abstract": {
"value": "We introduce a new family of toy problems to explore challenges with long context learning and associative recall in transformer models. Our setup involves interleaved segments of observations from randomly drawn linear deterministic dynamical systems. Each system is associated with a discrete symbolic label that must be learned in-context since these associations randomly shuffle between training instances.\n\nVia out-of-distribution experiments we find that learned next-token prediction for this toy problem involves at least two separate mechanisms. One \"label-based\" mechanism uses the discrete symbolic labels to do the associative recall required to predict the start of a resumption of a previously seen system's observations. The second ``observation-based'' mechanism largely ignores the discrete symbolic labels and performs a prediction based on the state observations previously seen in context. These two mechanisms have different learning dynamics: the second mechanism develops much earlier than the first.\n\nThe behavior of our toy model suggested concrete experiments that we performed with OLMo training checkpoints on an ICL translation task. We see a similar phenomenon: the model learns to continue a translation task in-context earlier than it decisively learns to in-context identify the meaning of a symbolic label telling it to translate."
},
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"title": {
"value": "Decomposing Prediction Mechanisms for In-context Recall"
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"value": "ICLR 2026 Conference Submission"
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2,026 | 053vZMxDB5 | [
2,
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"content": "This paper presents a reinforcement learning (RL) approach for learning from signal temporal logic (STL) to make learning more feasible for long-horizon tasks. The novel model-free approach divides and flattens complex STL formulas and searches for time-variable actualizations via Metropolis-Hasti... | {
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"content": {
"TLDR": {
"value": "We design a Reinforcement Learning framework based on time variables and task decomposition to solve Signal Temporal Logic tasks"
},
"_bibtex": {
"value": "@inproceedings{\nanonymous2025tgpo,\ntitle={{TGPO}: Temporal Grounded Policy Optimization for Signal Temporal Logic Tasks},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=053vZMxDB5},\nnote={under review}\n}"
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"abstract": {
"value": "Learning control policies for complex, long-horizon tasks is a central challenge in robotics and autonomous systems. Signal Temporal Logic (STL) offers a powerful and expressive language for specifying such tasks, but its non-Markovian nature and inherent sparse reward make it difficult to be solved via standard Reinforcement Learning (RL) algorithms. Prior RL approaches focus only on limited STL fragments or use STL robustness scores as sparse terminal rewards. In this paper, we propose TGPO, Temporal Grounded Policy Optimization, to solve general STL tasks. TGPO decomposes STL into timed subgoals and invariant constraints and provides a hierarchical framework to tackle the problem. The high-level component of TGPO proposes concrete time allocations for these subgoals, and the low-level time-conditioned policy learns to achieve the sequenced subgoals using a dense, stage-wise reward signal. During inference, we sample various time allocations and select the most promising assignment for the policy network to rollout the solution trajectory. To foster efficient policy learning for complex STL with multiple subgoals, we leverage the learned critic to guide the high-level temporal search via Metropolis-Hastings sampling, focusing exploration on temporally feasible solutions. We conduct experiments on five environments, ranging from low-dimensional navigation to manipulation, drone, and quadrupedal locomotion. Under a wide range of STL tasks, TGPO significantly outperforms state-of-the-art baselines (especially for high-dimensional and long-horizon cases), with an average of 31.6% improvement in task success rate compared to the best baseline."
},
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"value": "applications to robotics, autonomy, planning"
},
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"title": {
"value": "TGPO: Temporal Grounded Policy Optimization for Signal Temporal Logic Tasks"
},
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"value": "ICLR 2026 Conference Submission"
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2,026 | 05NHmcEpNk | [
8,
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"content": "This paper introduces CT-MLE, a model-based algorithm for continuous-time reinforcement learning (CTRL) that uses maximum likelihood estimation (MLE) of the state marginal density instead of directly modeling system dynamics.\nThe key idea is to achieve instance-dependent adaptivity, where the alg... | {
"cdate": 1758213925539,
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"TLDR": null,
"_bibtex": {
"value": "@inproceedings{\nanonymous2025instancedependent,\ntitle={Instance-Dependent Continuous-Time Reinforcement Learning via Maximum Likelihood Estimation},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=05NHmcEpNk},\nnote={under review}\n}"
},
"abstract": {
"value": "Continuous-time reinforcement learning (CTRL) provides a natural framework for sequential decision-making in dynamic environments where interactions evolve continuously over time. While CTRL has shown growing empirical success, its ability to adapt to varying levels of problem difficulty remains poorly understood. In this work, we investigate the instance-dependent behavior of CTRL and introduce a simple, model-based algorithm built on maximum likelihood estimation (MLE) with a general function approximator. Unlike existing approaches that estimate system dynamics directly, our method estimates the state marginal density to guide learning. We establish instance-dependent performance guarantees by deriving a regret bound that scales with the total reward variance and measurement resolution. Notably, the regret becomes independent of the specific measurement strategy when the observation frequency adapts appropriately to the problem’s complexity. To further improve performance, our algorithm incorporates a randomized measurement schedule that enhances sample efficiency without increasing measurement cost. These results highlight a new direction for designing CTRL algorithms that automatically adjust their learning behavior based on the underlying difficulty of the environment."
},
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"value": "learning theory"
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"title": {
"value": "Instance-Dependent Continuous-Time Reinforcement Learning via Maximum Likelihood Estimation"
},
"venue": {
"value": "ICLR 2026 Conference Submission"
},
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2,026 | 05PqjBzN6S | [
4,
2,
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{
"content": "This paper addresses the problem of determining when sufficient data is available to safely retrain a model after a sudden concept drift. The authors propose CALIPER, a model-agnostic and data-only test to estimate this required post-drift data size. The core idea is grounded in the concept of \"s... | {
"cdate": 1758350444098,
"content": {
"TLDR": null,
"_bibtex": {
"value": "@inproceedings{\nanonymous2025when,\ntitle={When to Retrain after Drift: A Data-Only Test of Post-Drift Data Size Sufficiency},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=05PqjBzN6S},\nnote={under review}\n}"
},
"abstract": {
"value": "Sudden concept drift makes previously trained predictors unreliable, yet deciding when to retrain and what post-drift data size is sufficient is rarely addressed. We propose CALIPER —a detector- and model-agnostic, data-only test that estimates the post-drift data size required for stable retraining. CALIPER exploits state dependence in streams generated by dynamical systems: we run a single-pass weighted local regression over the post-drift window and track a one-step proxy error as a function of a locality parameter $\\theta$. When an effective sample size gate is satisfied, a monotonically non-increasing trend in this error with increasing a locality parameter indicates that the data size is sufficiently informative for retraining.\nWe also provide a theoretical analysis of our CALIPER, and we show that the algorithm has a low per-update time and memory. Across datasets from four heterogeneous domains, three learner families, and two detectors, CALIPER consistently matches or exceeds the best fixed data size for retraining while incurring negligible overhead and often outperforming incremental updates. CALIPER closes the gap between drift detection and data-sufficient adaptation in streaming learning."
},
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"value": [
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"primary_area": {
"value": "learning on time series and dynamical systems"
},
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"title": {
"value": "When to Retrain after Drift: A Data-Only Test of Post-Drift Data Size Sufficiency"
},
"venue": {
"value": "ICLR 2026 Conference Submission"
},
"venueid": {
"value": "ICLR.cc/2026/Conference/Submission"
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2,026 | 05SHW9ai9e | [
4,
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{
"content": "To address DocQA limitations (single-modality bias, isolated RAG, long-document overload), this paper proposes MDocAgent—a framework integrating dual RAG (text via ColBERTv2, image via ColPali) and 5 collaborative agents (General, Critical, Text, Image, Summarizing). Evaluated on 5 benchmarks (MML... | {
"cdate": 1758214136657,
"content": {
"TLDR": null,
"_bibtex": {
"value": "@inproceedings{\nanonymous2025mdocagent,\ntitle={{MD}ocAgent: A Multi-Modal Multi-Agent Framework for Document Question Answering},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=05SHW9ai9e},\nnote={under review}\n}"
},
"abstract": {
"value": "Document Question Answering (DocQA) is a very common task. Existing methods using Large Language Models (LLMs) or Large Vision Language Models (LVLMs) and Retrieval Augmented Generation (RAG) often prioritize information from a single modal, failing to effectively integrate textual and visual cues. These approaches struggle with complex multi-modal reasoning, limiting their performance on real-world documents. We present MDocAgent (A Multi-Modal Multi-Agent Framework for Document Question Answering), a novel RAG and multi-agent framework that leverages both text and image. Our system employs five specialized agents: a general agent, a critical agent, a text agent, an image agent and a summarizing agent. These agents engage in multi-modal context retrieval, combining their individual insights to achieve a more comprehensive understanding of the document's content. This collaborative approach enables the system to synthesize information from both textual and visual components, leading to improved accuracy in question answering. Preliminary experiments on five benchmarks like MMLongBench, LongDocURL demonstrate the effectiveness of our MDocAgent, achieve an average improvement of 12.1% compared to current state-of-the-art method. This work contributes to the development of more robust and comprehensive DocQA systems capable of handling the complexities of real-world documents containing rich textual and visual information."
},
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"authors": null,
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"primary_area": {
"value": "applications to computer vision, audio, language, and other modalities"
},
"submission_guidelines": null,
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"title": {
"value": "MDocAgent: A Multi-Modal Multi-Agent Framework for Document Question Answering"
},
"venue": {
"value": "ICLR 2026 Conference Submission"
},
"venueid": {
"value": "ICLR.cc/2026/Conference/Submission"
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} | |
2,026 | 05THHF0w3y | [
0,
2,
4,
4
] | [
{
"content": "The paper proposes a new method for LLM reasoning, R-Capsule, where LLMs first output high-level plans which are in a latent space and then textual detailed steps and finally the answer. The authors choose several benchmarks on math reasoning (such as GSM-8k) and commensense reasoning (such as str... | {
"cdate": 1757406324840,
"content": {
"TLDR": null,
"_bibtex": {
"value": "@inproceedings{\nanonymous2025rcapsule,\ntitle={R-Capsule: Compressing High-Level Plans for Efficient Large Language Model Reasoning},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=05THHF0w3y},\nnote={under review}\n}"
},
"abstract": {
"value": "Chain-of-Thought (CoT) prompting has enabled Large Language Models (LLMs) to tackle complex reasoning tasks by generating explicit step-by-step rationales. However, this verbosity incurs significant computational overhead in terms of latency and memory, and can lead to error propagation over long reasoning chains. We propose the \\textbf{Reasoning Capsule}, a novel framework that captures the efficiency of latent reasoning while retaining the transparency of explicit CoT. Our core idea is to compress the high-level strategic plan of a reasoning process into a compact, low-dimensional latent representation---the Reasoning Capsule---while leaving the low-level execution steps explicit. This hybrid approach is grounded in the Information Bottleneck principle, where we learn a capsule that is a \\emph{minimal sufficient statistic} for the reasoning task. Minimality is enforced structurally via a low-dimensional bottleneck, ensuring efficiency. Sufficiency is enforced via a dual-objective function: a primary task loss for answer accuracy and an auxiliary reconstruction loss that ensures the capsule faithfully represents the original textual plan. This reconstruction objective grounds the latent space, making the compressed plan interpretable and robust against uninformative shortcuts. Our framework unifies efficiency, accuracy, and interpretability, significantly reducing the token footprint of reasoning while maintaining or improving performance on complex reasoning benchmarks."
},
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"authors": null,
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"primary_area": {
"value": "foundation or frontier models, including LLMs"
},
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"title": {
"value": "R-Capsule: Compressing High-Level Plans for Efficient Large Language Model Reasoning"
},
"venue": {
"value": "ICLR 2026 Conference Submission"
},
"venueid": {
"value": "ICLR.cc/2026/Conference/Submission"
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2,026 | 05hNleYOcG | [
2,
4,
2,
2
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{
"content": "The paper introduces PLAGUE, a plug-and-play framework for designing multi-turn jailbreak attacks on large language models (LLMs). Inspired by lifelong-learning and agentic architectures, PLAGUE divides the attack process into three stages — Planner, Primer, and Finisher — enabling adaptable and m... | {
"cdate": 1758135059535,
"content": {
"TLDR": {
"value": "Agentic framework for discovering novel potent multi-turn jailbreak attacks that achieve an attack success rate of 67.3% on Claude Opus 4.1"
},
"_bibtex": {
"value": "@inproceedings{\nanonymous2025plague,\ntitle={{PLAGUE}: Plug-and-play Framework for Lifelong Adaptive Generation of Multi-turn Exploits},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=05hNleYOcG},\nnote={under review}\n}"
},
"abstract": {
"value": "Large Language Models (LLMs) are improving at an exceptional rate. With the advent of agentic workflows, multi-turn dialogue has become the de facto mode of interaction with LLMs for completing long and complex tasks. While LLM capabilities continue to improve, they remain increasingly susceptible to jailbreaking, especially in multi-turn scenarios where harmful intent can be subtly injected across the conversation to produce nefarious outcomes. While single-turn attacks have been extensively explored, adaptability, efficiency and effectiveness continue to remain key challenges for their multi-turn counterparts. To address these gaps, we present PLAGUE, a novel plug-and-play framework for designing multi-turn attacks inspired by lifelong-learning agents. PLAGUE dissects the lifetime of a multi-turn attack into three carefully designed phases (Primer, Planner and Finisher) that enable a systematic and information-rich exploration of the multi-turn attack family. Evaluations show that red-teaming agents designed using PLAGUE achieve state-of-the-art jailbreaking results, improving attack success rates (ASR) by more than 30% across leading models in a lesser or comparable query budget. Particularly, PLAGUE enables an ASR (based on StrongReject) of 81.4% on OpenAI's o3 and 67.3% on Claude's Opus 4.1, two models that are considered highly resistant to jailbreaks in safety literature. Our work offers tools and insights to understand the importance of plan initialization, context optimization, and lifelong learning in crafting multi-turn attacks for a comprehensive model vulnerability evaluation."
},
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"authorids": null,
"authors": null,
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"keywords": {
"value": [
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"Agentic AI"
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"value": "/pdf/de8dc0979b8266f26b81ee913344d9abba387bb0.pdf"
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"primary_area": {
"value": "alignment, fairness, safety, privacy, and societal considerations"
},
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"value": "/attachment/da1b9d173949372d38df20cfd54baf183ccdf1be.zip"
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"title": {
"value": "PLAGUE: Plug-and-play Framework for Lifelong Adaptive Generation of Multi-turn Exploits"
},
"venue": {
"value": "ICLR 2026 Conference Submission"
},
"venueid": {
"value": "ICLR.cc/2026/Conference/Submission"
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2,026 | 05pfP2khzx | [
2,
2,
4
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{
"content": "This paper introduces VIDEOREPAIR, a video refinement framework to correct text-video misalignments. It has three steps: 1. detect misalignment. Finding the issue and region with MLLM. 2. Plan the refinement including preserve the correct parts and construct prompts that could be used to re-genera... | {
"cdate": 1758222291968,
"content": {
"TLDR": null,
"_bibtex": {
"value": "@misc{\nlee2025selfcorrecting,\ntitle={Self-Correcting Text-to-Video Generation with Misalignment Detection and Localized Refinement},\nauthor={Daeun Lee and Jaehong Yoon and Jaemin Cho and Mohit Bansal},\nyear={2025},\nurl={https://openreview.net/forum?id=05pfP2khzx}\n}"
},
"abstract": {
"value": "Recent text-to-video (T2V) diffusion models have made remarkable progress in\ngenerating high-quality and diverse videos. However, they often struggle to align\nwith complex text prompts, particularly when multiple objects, attributes, or spatial\nrelations are specified. We introduce VideoRepair, the first self-correcting,\ntraining-free, and model-agnostic video refinement framework that automatically\ndetects fine-grained text–video misalignments and performs targeted, localized\ncorrections. Our key insight is that even misaligned videos usually contain correctly\nrendered regions that should be preserved rather than regenerated. Building on this\nobservation, VideoRepair proposes a novel region-preserving refinement strategy\nwith three stages: (i) misalignment detection, where systematic MLLM-based evaluation\nwith automatically generated spatio-temporal questions identifies faithful\nand misaligned regions; (ii) refinement planning, which preserves correctly generated\nentities, segments their regions across frames, and constructs targeted prompts\nfor misaligned areas; and (iii) localized refinement, which selectively regenerates\nproblematic regions while preserving faithful content through joint optimization\nof preserved and newly generated areas. This self-correcting, region-preserving\nstrategy converts evaluation signals into actionable guidance for refinement, enabling\nefficient and interpretable corrections. On two challenging benchmarks,\nEvalCrafter and T2V-CompBench, VideoRepair achieves substantial improvements\nover recent baselines across diverse alignment metrics. Comprehensive\nablations further demonstrate the efficiency, robustness, and interpretability of our\nframework."
},
"anonymous_url": null,
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"value": [
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"~Jaehong_Yoon1",
"~Jaemin_Cho1",
"~Mohit_Bansal2"
]
},
"authors": {
"value": [
"Daeun Lee",
"Jaehong Yoon",
"Jaemin Cho",
"Mohit Bansal"
]
},
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"keywords": {
"value": [
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"Multi-agent"
]
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"value": "lee|selfcorrecting_texttovideo_generation_with_misalignment_detection_and_localized_refinement"
},
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"primary_area": {
"value": "applications to computer vision, audio, language, and other modalities"
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"value": "/attachment/d49f3262bd569432cfbb01e316e81fba9e473798.zip"
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"title": {
"value": "Self-Correcting Text-to-Video Generation with Misalignment Detection and Localized Refinement"
},
"venue": {
"value": "ICLR 2026 Conference Withdrawn Submission"
},
"venueid": {
"value": "ICLR.cc/2026/Conference/Withdrawn_Submission"
}
},
"forum": "05pfP2khzx",
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} | |
2,026 | 05uq3XUJaT | [
2,
2,
4
] | [
{
"content": "This paper introduces a listwise fine-tuning method for LLM-based text reranking. The method improves three limitations of existing LLM rankers (single-token compression, shallow scoring heads, and pairwise objectives).",
"id": "DvaKUEhgPp",
"rating": 2
},
{
"content": "This paper ... | {
"cdate": 1757411444566,
"content": {
"TLDR": {
"value": "We propose a method to improve the fine-tuning performance of text ranking models by leveraging feature fusion, incorporating customized MLP modules, and optimizing with a listwise loss."
},
"_bibtex": {
"value": "@misc{\nsong2025finetuning,\ntitle={Fine-tuning large language models for text ranking with listwise constraints},\nauthor={Jiawen Song and Bingfei Zhang and Sai Gao and Xueyao Zhang and Wenqing Xu and Guanyu Chen and Junwei Xing and Hui Li and Yunpeng Peng and Zhi Zang},\nyear={2025},\nurl={https://openreview.net/forum?id=05uq3XUJaT}\n}"
},
"abstract": {
"value": "With the rapid adoption of large language models (LLMs) across diverse applications, retrieval augmentation has become a key factor for improving downstream performance. Recent advances show that LLM-based retrieval can substantially enhance ranking quality. In this work, we present a novel LLM-based retrieval framework optimized along three complementary dimensions: (1) a customized attention-based fusion of hidden-layer representations, (2) a dedicated multi-layer perceptron (MLP) module for enriched feature transformation, and (3) a new list-wise learning objective, ListRank loss, to capture fine-grained relevance order. Experimental results demonstrate that our model achieves state-of-the-art performance. The model is publicly available for download on HuggingFace."
},
"anonymous_url": null,
"authorids": {
"value": [
"~Jiawen_Song1",
"~Bingfei_Zhang1",
"~Sai_Gao1",
"~Xueyao_Zhang2",
"~Wenqing_Xu3",
"~Guanyu_Chen14",
"~Junwei_Xing1",
"~Hui_Li58",
"~Yunpeng_Peng2",
"~Zhi_Zang1"
]
},
"authors": {
"value": [
"Jiawen Song",
"Bingfei Zhang",
"Sai Gao",
"Xueyao Zhang",
"Wenqing Xu",
"Guanyu Chen",
"Junwei Xing",
"Hui Li",
"Yunpeng Peng",
"Zhi Zang"
]
},
"code_of_ethics": null,
"keywords": {
"value": [
"Feature fusion",
"listwise",
"LLM",
"rank"
]
},
"no_acknowledgement_section": null,
"paperhash": {
"value": "song|finetuning_large_language_models_for_text_ranking_with_listwise_constraints"
},
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"value": "/pdf/438531bfdc6d7eff6df3c9f4faf576cb9faa1f30.pdf"
},
"primary_area": {
"value": "unsupervised, self-supervised, semi-supervised, and supervised representation learning"
},
"submission_guidelines": null,
"supplementary_material": null,
"title": {
"value": "Fine-tuning large language models for text ranking with listwise constraints"
},
"venue": {
"value": "ICLR 2026 Conference Withdrawn Submission"
},
"venueid": {
"value": "ICLR.cc/2026/Conference/Withdrawn_Submission"
}
},
"forum": "05uq3XUJaT",
"id": "05uq3XUJaT",
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} | |
2,026 | 0694m9ixnv | [
4,
6,
2
] | [
{
"content": "This paper introduces Instruction Distillation, a new paradigm for improving the quality of low-quality instruction-following data. The authors propose a dataset called MIXTURE that maps multiple low-quality or redundant text inputs to a distilled high-quality target. Building on this dataset, the... | {
"cdate": 1758008662115,
"content": {
"TLDR": null,
"_bibtex": {
"value": "@inproceedings{\nanonymous2025lmmixup,\ntitle={{LM}-mixup: Text Data Augmentation via Language Model based Mixup},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=0694m9ixnv},\nnote={under review}\n}"
},
"abstract": {
"value": "Instruction tuning is crucial for aligning Large Language Models (LLMs), yet the quality of instruction-following data varies significantly. While high-quality data is paramount, it is often scarce; conversely, abundant low-quality data is frequently discarded, leading to substantial information loss. Existing data augmentation methods struggle to augment this low-quality data effectively, and the evaluation of such techniques remains poorly defined. To address this, we formally define the task of *Instruction Distillation*: distilling multiple low-quality and redundant inputs into high-quality and coherent instruction-output pairs. Specifically, we introduce a comprehensive data construction pipeline to create *MIXTURE*, a 144K-sample dataset pairing low-quality or semantically redundant imperfect instruction clusters with their high-quality distillations. We then introduce *LM-Mixup*, by first performing supervised fine-tuning on *MIXTURE* and then optimizing it with reinforcement learning. This process uses three complementary reward signals: quality, semantic alignment, and format compliance, via Group Relative Policy Optimization (GRPO). We demonstrate that *LM-Mixup* effectively augments imperfect datasets: fine-tuning LLMs on its distilled data, which accounts for only about 3% of the entire dataset, not only surpasses full-dataset training but also competes with state-of-the-art high-quality data selection methods across multiple benchmarks. Our work establishes that low-quality data is a valuable resource when properly distilled and augmented with *LM-Mixup*, significantly enhancing the efficiency and performance of instruction-tuned LLMs."
},
"anonymous_url": null,
"authorids": null,
"authors": null,
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"keywords": {
"value": [
"Instruction distillation",
"LM mixup"
]
},
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"pdf": {
"value": "/pdf/063db25688cafc17b63b0a73cc99a225f64ae83e.pdf"
},
"primary_area": {
"value": "alignment, fairness, safety, privacy, and societal considerations"
},
"submission_guidelines": null,
"supplementary_material": null,
"title": {
"value": "LM-mixup: Text Data Augmentation via Language Model based Mixup"
},
"venue": {
"value": "ICLR 2026 Conference Submission"
},
"venueid": {
"value": "ICLR.cc/2026/Conference/Submission"
}
},
"forum": "0694m9ixnv",
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} | |
2,026 | 06I7jcrkW2 | [
6,
6,
4,
8
] | [
{
"content": "This paper tackles the important and challenging problem of accelerating Real-Time TDDFT (RT-TDDFT) computations using deep learning. \nSpecifically, it adopts an autoregressive framework to accelerate the propagations of RT-TDDFT, where the wavefunctions of previous steps are input into the netw... | {
"cdate": 1758291547393,
"content": {
"TLDR": null,
"_bibtex": {
"value": "@inproceedings{\nanonymous2025orbital,\ntitle={Orbital Transformers for Predicting Wavefunctions in Time-Dependent Density Functional Theory},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2025},\nurl={https://openreview.net/forum?id=06I7jcrkW2},\nnote={under review}\n}"
},
"abstract": {
"value": "We aim to learn wavefunctions simulated by time-dependent density functional theory (TDDFT), which can be efficiently represented as linear combination coefficients of atomic orbitals. In real-time TDDFT, the electronic wavefunctions of a molecule evolve over time in response to an external excitation, enabling first-principles predictions of physical properties such as optical absorption, electron dynamics, and high-order response. However, conventional real-time TDDFT relies on time-consuming propagation of all occupied states with fine time steps. In this work, we propose OrbEvo, which is based on an equivariant graph transformer architecture and learns to evolve the full electronic wavefunction coefficients across time steps. First, to account for external field, we design an equivariant conditioning to encode both strength and direction of external electric field and break the symmetry from SO(3) to SO(2). Furthermore, we design two OrbEvo models, OrbEvo-FullWF and OrbEvo-DM, using wavefunction pooling and density matrix as interaction method, respectively. Motivated by the central role of the density functional in TDDFT, OrbEvo-DM encodes the density matrix aggregated from all occupied electronic states into feature vectors via tensor contraction, providing a more intuitive approach to learn the time evolution operator. We adopt a training strategy specifically tailored to limit the error accumulation of time-dependent wavefunctions over autoregressive rollout. To evaluate our approach, we generate TDDFT datasets consisting of 5,000 different molecules in the QM9 dataset and 1,500 molecular configurations of the malonaldehyde molecule in the MD17 dataset. Results show that our OrbEvo model accurately captures quantum dynamics of excited states under external field, including time-dependent wavefunctions, time-dependent dipole moment, and optical absorption spectra characterized by dipole oscillator strength. It also shows strong generalization capability on the diverse molecules in the QM9 dataset."
},
"anonymous_url": null,
"authorids": null,
"authors": null,
"code_of_ethics": null,
"keywords": {
"value": [
"Machine learning density functional theory",
"Time dependent neural PDE solver"
]
},
"no_acknowledgement_section": null,
"paperhash": null,
"pdf": {
"value": "/pdf/b9b9470edaaf38e546adf996fb79f0e4341c771e.pdf"
},
"primary_area": {
"value": "applications to physical sciences (physics, chemistry, biology, etc.)"
},
"submission_guidelines": null,
"supplementary_material": null,
"title": {
"value": "Orbital Transformers for Predicting Wavefunctions in Time-Dependent Density Functional Theory"
},
"venue": {
"value": "ICLR 2026 Conference Submission"
},
"venueid": {
"value": "ICLR.cc/2026/Conference/Submission"
}
},
"forum": "06I7jcrkW2",
"id": "06I7jcrkW2",
"invitations": [
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"ICLR.cc/2026/Conference/Submission18854/-/Full_Submission"
],
"license": "CC BY 4.0",
"mdate": 1759897077611,
"odate": 1759896705795,
"readers": [
"everyone"
],
"signatures": [
"ICLR.cc/2026/Conference/Submission18854/Authors"
],
"writers": [
"ICLR.cc/2026/Conference",
"ICLR.cc/2026/Conference/Submission18854/Authors"
]
} |
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