year int64 2.03k 2.03k | id stringlengths 10 10 | rating listlengths 0 9 | decision stringclasses 1
value | reviewer_comments listlengths 0 9 | _raw_metadata dict |
|---|---|---|---|---|---|
2,026 | 00F7BfXLYJ | [
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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, ... | {
"cdate": 1757998013559,
"content": {
"TLDR": null,
"_bibtex": {
"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 Lea... | |
2,026 | 00HNN8O7Ni | [
4,
2,
2,
4
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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,
"content": {
"TLDR": {
"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."
},
"_bibtex": {
"value": "@inproceedings{\nano... | |
2,026 | 00UQtHqB2k | [
2,
6,
2,
4
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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... | {
"cdate": 1758232139112,
"content": {
"TLDR": null,
"_bibtex": {
"value": "@inproceedings{\nanonymous2025toward,\ntitle={Toward Unifying Group Fairness Evaluation from a Sparsity Perspective},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representa... | |
2,026 | 017F77AYeQ | [
2,
2,
4,
0
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{
"content": "The paper proposes SMART-3D, a mask token modeling approach for 3D generation.",
"id": "gZowcvNNqh",
"rating": 2
},
{
"content": "The paper proposes an framework that merges masked autoregressive generation with diffusion modeling and linear attention, addressing key efficiency bot... | {
"cdate": 1758113495159,
"content": {
"TLDR": null,
"_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 L... | |
2,026 | 023yMrtHQP | [
4,
4,
4
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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... | {
"cdate": 1758292986416,
"content": {
"TLDR": null,
"_bibtex": {
"value": "@inproceedings{\nanonymous2025expectationevidence,\ntitle={Expectation{\\textendash}Evidence Prompting: Structuring Verification by Comparing Expected and Observed Evidence},\nauthor={Anonymous},\nbooktitle={Submitted to The F... | |
2,026 | 02NbD16OnA | [
4,
4,
4,
6
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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,
"content": {
"TLDR": {
"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": "@inp... | |
2,026 | 02cEkpURXH | [
2,
2,
6,
4
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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... | {
"cdate": 1758311939461,
"content": {
"TLDR": null,
"_bibtex": {
"value": "@inproceedings{\nanonymous2025early,\ntitle={Early Layer Readouts for Robust Knowledge Distillation},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference on Learning Representations},\nyear={2... | |
2,026 | 02mBAZjFzp | [
4,
4,
4,
6
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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... | {
"cdate": 1758296070926,
"content": {
"TLDR": {
"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": {
... | |
2,026 | 02mgFnnfqG | [
4,
8,
6,
6
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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... | {
"cdate": 1757934812324,
"content": {
"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... | |
2,026 | 032sg6mGp9 | [
4,
4,
6,
6
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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... | {
"cdate": 1758285923748,
"content": {
"TLDR": null,
"_bibtex": {
"value": "@inproceedings{\nanonymous2025identifiability,\ntitle={Identifiability in Noisy Label Learning: A Multinomial Mixture Modelling Approach},\nauthor={Anonymous},\nbooktitle={Submitted to The Fourteenth International Conference o... | |
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... | {
"cdate": 1757189578927,
"content": {
"TLDR": null,
"_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}"
},
... | |
2,026 | 03MfCNn3pF | [
2,
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2,
6
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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."
},
"_bibte... | |
2,026 | 03QzvMzxVM | [
2,
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4
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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={Anony... | |
2,026 | 03ccrSpjOx | [
4,
4,
4,
6
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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,
"content": {
"TLDR": null,
"_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... | |
2,026 | 03fFxN6Orj | [
4,
2,
4
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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... | |
2,026 | 03jzVlLxEe | [
6,
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4,
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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,
"content": {
"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... | |
2,026 | 03qTI3NKqi | [
4,
4,
4,
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... | {
"cdate": 1758191821554,
"content": {
"TLDR": null,
"_bibtex": {
"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 Represe... | |
2,026 | 03u504EDJp | [
2,
4,
6,
2,
2
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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... | {
"cdate": 1756744193214,
"content": {
"TLDR": null,
"_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 Confe... | |
2,026 | 040ClRXMf3 | [
6,
8,
2,
8
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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... | {
"cdate": 1758298867680,
"content": {
"TLDR": {
"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... | |
2,026 | 04HwYGgp2w | [
6,
8,
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,
"content": {
"TLDR": null,
"_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 Learni... | |
2,026 | 04JkPDiCnp | [
2,
6,
4,
2
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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,
"content": {
"TLDR": null,
"_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 L... | |
2,026 | 04Tfwy3LLC | [
2,
6,
4,
8
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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={Reasse... | |
2,026 | 04h40hEgTj | [
6,
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2,
4
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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."
},
"... | |
2,026 | 053vZMxDB5 | [
2,
8,
4
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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... | {
"cdate": 1756884774931,
"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 ... | |
2,026 | 05NHmcEpNk | [
8,
4,
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,
"content": {
"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 ... | |
2,026 | 05PqjBzN6S | [
4,
2,
6
] | [
{
"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 ... | |
2,026 | 05SHW9ai9e | [
4,
2,
4,
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 Learn... | |
2,026 | 05THHF0w3y | [
0,
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4,
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{
"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 Le... | |
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-p... | |
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={h... | |
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{\nsong2025fin... | |
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 Representatio... | |
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 Conferen... |
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