GenAI4ELab/papercli-papers huggingface dataset:
MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents
Authors: Zijian Zhou; Ao Qu; Zhaoxuan Wu; Sunghwan Kim; Alok Prakash; Daniela Rus; Bryan Kian Hsiang Low; Paul Pu Liang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Modern language agents often need to solve long-horizon tasks requiring multiple turns of interactions with the environment, where they retrieve external information, adapt to observations, and answer interdependent queries. Yet, most LLM systems rely on full-context prompting, appending all past turns regardless of their relevance. This leads to un-bounded memory growth, increased computational costs, and degraded reasoning performance on out-of-distribution input lengths due to LLM forgetting the context. We introduce MEM1, an end-to-end reinforcement learning framework that enables agents to operate with near constant context size when solving long-horizon tasks. At each turn, MEM1 updates a compact shared internal state that jointly supports memory consolidation and reasoning. Leveraging reinforcement learning (RL) and rollout trajectory truncation, we train a MEM1 agent to develop internal states that integrate prior memory with new observations from the environment while strategically discarding irrelevant or redundant information. Experiments across three domains, including internal retrieval QA, open-domain web QA, and multi-turn web shopping, show that MEM1-7B improves performance by 3.5$\times$ while reducing memory usage by 3.7$\times$ compared to Qwen2.5-14B-Instruct on an augmented multi-hop QA dataset with 16 objectives in each task, and generalizes beyond the training horizon. Our results demonstrate the promise of reasoning-driven memory consolidation as a scalable alternative to existing solutions for training long-horizon task-solving agents that involve multiple interactions, where both efficiency and performance are optimized. Code can be found at https://github.com/MIT-MI/MEM1.
On Coreset for LASSO Regression Problem with Sensitivity Sampling
Authors: YuanBin Zou; Junyu Huang; Jianxin Wang; Qilong Feng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In this paper, we study coreset construction for LASSO regression, where a coreset is a small, weighted subset of the data that approximates the original problem with provable guarantees. For unregularized regression problems, sensitivity sampling is a successful and widely applied technique for constructing coresets. However, extending these methods to LASSO typically requires coreset size to scale with O(\mathcal{G}d), where d is the VC dimension and \mathcal{G} is the total sensitivity, following existing generalization bounds. A key challenge in improving upon this general bound lies in the difficulty of capturing the sparse and localized structure of the function space induced by the \ell_1 penalty in LASSO objective. To address this, we first provide an empirical process-based method of sensitivity sampling for LASSO, localizing the procedure by decomposing the functional space into separate components, which leads to tighter estimation error. By carefully leveraging the geometric properties of these localized spaces, we establish tight empirical process bounds on the required coreset size. These techniques enable us to achieve a coreset of size \tilde{O}(\epsilon^{-2}d\cdot(\log^3 d\cdot\min\{1,\log d/\lambda^2\}+\log(1/\delta))), which ensures a  (1\pm\epsilon)-approximation for any \epsilon,\delta\in(0,1) and \lambda > 0. Furthermore, we give a lower bound showing that any algorithm achieving a (1+\epsilon)-approximation must select at least $Omega(\frac{d\log{d}}{\epsilon^2}) rows in the regime where \lambda=O(d^{-1/2}). Empirical experiments show that our proposed algorithm is at least 4 times faster than the existing LASSO solver and more than 9 times faster on half of the datasets, while ensuring high solution quality and sparsity.
Grounding or Guessing? Visual Signals for Detecting Hallucinations in Sign Language Translation
Authors: Yasser HAMIDULLAH; Koel Dutta Chowdhury; Yusser Al Ghussin; Shakib Yazdani; Cennet Oguz; Josef van Genabith; Cristina España-Bonet
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Hallucination, where models generate fluent text unsupported by visual evidence, remains a major flaw in vision–language models and is particularly critical in sign language translation (SLT). In SLT, meaning depends on precise grounding in video, and gloss-free models are especially vulnerable because they map continuous signer movements directly into natural language without intermediate gloss supervision that serves as alignment. We argue that hallucinations arise when models rely on language priors rather than visual input.
To capture this, we propose a token-level reliability measure that quantifies how much the decoder uses visual information. Our method combines feature-based sensitivity, which measures internal changes when video is masked, with counterfactual signals, which capture probability differences between clean and altered video inputs. These signals are aggregated into a sentence-level reliability score, providing a compact and interpretable measure of visual grounding. 
We evaluate the proposed measure on two SLT benchmarks (PHOENIX-2014T and CSL-Daily) with both gloss-based and gloss-free models. Our results show that reliability predicts hallucination rates, generalizes across datasets and architectures, and decreases under visual degradations. Beyond these quantitative trends, we also find that reliability distinguishes grounded tokens from guessed ones, allowing risk estimation without references; when combined with text-based signals (confidence, perplexity, or entropy), it further improves hallucination risk estimation.  Qualitative analysis highlights why gloss-free models are more susceptible to hallucinations. Taken together, our findings establish reliability as a practical and reusable tool for diagnosing hallucinations in SLT, and lay the groundwork for more robust hallucination detection in multimodal generation.
Universal Value-Function Uncertainties
Authors: Moritz Akiya Zanger; Max Weltevrede; Yaniv Oren; Pascal R. Van der Vaart; Caroline Horsch; Wendelin Boehmer; Matthijs T. J. Spaan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Estimating epistemic uncertainty in value functions is a crucial challenge for many aspects of reinforcement learning (RL), including efficient exploration, safe decision-making, and offline RL. While deep ensembles provide a robust method for quantifying value uncertainty, they come with significant computational overhead. Single-model methods, while computationally favorable, often rely on heuristics and typically require additional propagation mechanisms for myopic uncertainty estimates. In this work we introduce universal value-function uncertainties (UVU), which, similar in spirit to random network distillation (RND), quantify uncertainty as squared prediction errors between an online learner and a fixed, randomly initialized target network. Unlike RND, UVU errors reflect policy-conditional $\textit{value uncertainty}$, incorporating the future uncertainties $\textit{any policy}$ may encounter. This is due to the training procedure employed in UVU: the online network is trained using temporal difference learning with a synthetic reward derived from the fixed, randomly initialized target network. We provide an extensive theoretical analysis of our approach using neural tangent kernel (NTK) theory and show that in the limit of infinite network width, UVU errors are exactly equivalent to the variance of an ensemble of independent universal value functions. Empirically, we show that UVU achieves equal performance to large ensembles on challenging multi-task offline RL settings, while offering simplicity and substantial computational savings.
Sampling Complexity of TD and PPO in RKHS
Authors: LU ZOU; Wendi Ren; WEIZHONG ZHANG; Liang Ding; Shuang Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We revisit Proximal Policy Optimization (PPO) from a function-space perspective. 
Our analysis decouples policy evaluation and improvement in a reproducing kernel Hilbert space (RKHS): 
(i)  A kernelized temporal-difference (TD) critic performs efficient RKHS-gradient updates using only one-step state–action transition samples.
(ii) a KL-regularized, natural-gradient policy step exponentiates the evaluated action-value, recovering a PPO/TRPO-style proximal update in continuous state-action spaces. 
We provide non-asymptotic, instance-adaptive guarantees whose rates depend on RKHS entropy, unifying tabular, linear, Sobolev, Gaussian, and Neural Tangent Kernel (NTK) regimes, and we derive a sampling rule for the proximal update that ensures the optimal $k^{-1/2}$  convergence rate for stochastic optimization.
Empirically, the theory-aligned schedule improves stability and sample efficiency on common control tasks (e.g., CartPole, Acrobot, and HalfCheetah), while our TD-based critic attains favorable throughput versus a GAE baseline. 
Altogether, our results place PPO on a firmer theoretical footing beyond finite-dimensional assumptions and clarify when RKHS-proximal updates with kernel-TD critics yield global policy improvement with practical efficiency.
The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity?
Authors: Alexander Hägele; Aryo Pradipta Gema; Henry Sleight; Ethan Perez; Jascha Sohl-Dickstein
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: As AI becomes more capable, we entrust it with more general and consequential tasks. The risks from failure grow more severe with increasing task scope. It is therefore important to understand 
the ways extremely capable AI models will fail: Will they fail by systematically pursuing goals we do not intend? Or will they fail by being a hot mess, and taking nonsensical actions that do not further any goal? We operationalize this question using a bias-variance decomposition of the errors made by AI models: An AI's *error incoherence* on a task is measured over test-time randomness as the fraction of its error that stems from variance rather than bias in task outcome. Across all tasks and frontier models we measure, we find that the longer models spend reasoning and taking actions, *the more incoherent* their failures become. We observe that error incoherence changes with model scale in a way that is task and experiment dependent. However, in several settings larger, more capable models are more incoherent than smaller models.
Consequently, scale alone seems unlikely to eliminate incoherence. Instead, as more capable AIs pursue harder tasks, requiring more sequential action and thought, our results predict failures to be accompanied by more incoherent behavior. 
This suggests a future where AIs sometimes cause industrial accidents (due to unpredictable misbehavior), but are less likely to exhibit consistent pursuit of a misaligned goal. 
This increases the relative importance of alignment research targeting reward hacking or goal misspecification.
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model
Authors: Moritz Akiya Zanger; Pascal R. Van der Vaart; Wendelin Boehmer; Matthijs T. J. Spaan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Uncertainty quantification is a critical aspect of reinforcement learning and deep learning, with numerous applications ranging from efficient exploration and stable offline reinforcement learning to outlier detection in medical diagnostics. The scale of modern neural networks, however, complicates the use of many theoretically well-motivated approaches such as full Bayesian inference. Approximate methods like deep ensembles can provide reliable uncertainty estimates but still remain computationally expensive. In this work, we propose contextual similarity distillation, a novel approach that explicitly estimates the variance of an ensemble of deep neural networks with a single model, without ever learning or evaluating such an ensemble in the first place. Our method builds on the predictable learning dynamics of wide neural networks, governed by the neural tangent kernel, to derive an efficient approximation of the predictive variance of an infinite ensemble. Specifically, we reinterpret the computation of ensemble variance as a supervised regression problem with kernel similarities as regression targets. The resulting model can estimate predictive variance at inference time with a single forward pass, and can make use of unlabeled target-domain data or data augmentations to refine its uncertainty estimates. We empirically validate our method across a variety of out-of-distribution detection benchmarks and sparse-reward reinforcement learning environments. We find that our single-model method performs competitively and sometimes superior to ensemble-based baselines and serves as a reliable signal for efficient exploration. These results, we believe, position contextual similarity distillation as a principled and scalable alternative for uncertainty quantification in reinforcement learning and general deep learning.
Tools are under-documented: Simple Document Expansion Boosts Tool Retrieval
Authors: Xuan Lu; Haohang Huang; Rui Meng; Yaohui Jin; Wenjun Zeng; Xiaoyu Shen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) have recently demonstrated strong capabilities in tool use, yet progress in tool retrieval remains hindered by incomplete and heterogeneous tool documentation. To address this challenge, we introduce Tool-REX, a new benchmark and framework that systematically enriches tool documentation with structured fields to enable more effective tool retrieval,  together with two dedicated models, Tool-Embed and Tool-Rank. We design a scalable document expansion pipeline that leverages both open- and closed-source LLMs to generate, validate, and refine enriched tool profiles at low cost, producing large-scale corpora with 50k instances for 
embedding-based retrievers and 200k for rerankers. On top of this data, we develop two models specifically tailored for tool retrieval: Tool-Embed, a dense retriever, and Tool-Rank, an LLM-based reranker. Extensive experiments on ToolRet and Tool-REX demonstrate that document expansion substantially improves retrieval performance, with Tool-Embed and Tool-Rank achieving new state-of-the-art results on both benchmarks. We further analyze the contribution of individual fields to retrieval effectiveness, as well as the broader impact of document expansion on both training and evaluation. Overall, our findings highlight both the promise and limitations of LLM-driven document expansion, positioning \textsc{Tool-REX}, along with the proposed Tool-Embed and Tool-Rank, as a foundation for future research in tool retrieval.
Exploring Interpretability for Visual Prompt Tuning with Cross-layer Concepts
Authors: Yubin Wang; XINYANG JIANG; De Cheng; Xiangqian Zhao; Zilong Wang; Dongsheng Li; Cairong Zhao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Visual prompt tuning offers significant advantages for adapting pre-trained visual foundation models to specific tasks. However, current research provides limited insight into the interpretability of this approach, which is essential for enhancing AI reliability and enabling AI-driven knowledge discovery. In this paper, rather than learning abstract prompt embeddings, we propose the first framework, named Interpretable Visual Prompt Tuning (IVPT), to explore interpretability for visual prompts by introducing cross-layer concept prototypes. Specifically, visual prompts are linked to human-understandable semantic concepts, represented as a set of category-agnostic prototypes, each corresponding to a specific region of the image. IVPT then aggregates features from these regions to generate interpretable prompts for multiple network layers, allowing the explanation of visual prompts at different network depths and semantic granularities. Comprehensive qualitative and quantitative evaluations on fine-grained classification benchmarks show its superior interpretability and performance over visual prompt tuning methods and existing interpretable methods. Our code is available at https://github.com/ThomasWangY/IVPT.
Adaptive Collaboration with Humans: Metacognitive Policy Optimization for Multi-Agent LLMs with Continual Learning
Authors: Wei Yang; Defu Cao; Jiacheng Pang; Muyan Weng; Yan Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While scaling individual Large Language Models (LLMs) has delivered remarkable progress, the next frontier lies in scaling collaboration through multi-agent systems (MAS). However, purely autonomous MAS remain ``closed-world'' systems, constrained by the static knowledge horizon of pre-trained models. This limitation makes them brittle on tasks requiring knowledge beyond training data, often leading to collective failure under novel challenges. To address this, we propose the Human-In-the-Loop Multi-Agent Collaboration (HILA) framework, a principled paradigm for human--agent collaboration. HILA trains agents to learn a metacognitive policy that governs when to solve problems autonomously and when to defer to a human expert. To operationalize this policy, we introduce Dual-Loop Policy Optimization, which disentangles immediate decision-making from long-term capability growth. The inner loop applies Group Relative Policy Optimization (GRPO) with a cost-aware reward to optimize deferral decisions, while the outer loop implements continual learning, transforming expert feedback into high-quality supervised signals that strengthen the agent's reasoning ability. Experiments on challenging mathematical and problem-solving benchmarks show that HILA, equipped with Dual-Loop Policy Optimization, consistently outperforms advanced MAS, establishing a principled foundation for collaborative and continually improving agentic systems.
WebDevJudge: Evaluating (M)LLMs as Critiques for Web Development Quality
Authors: Chunyang Li; Yilun Zheng; Xinting Huang; Tianqing Fang; Jiahao Xu; Lihui Chen; Yangqiu Song; Han Hu
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: The paradigm of LLM-as-a-judge is emerging as a scalable and efficient alternative to human evaluation, demonstrating strong performance on well-defined tasks. However, its reliability in open-ended tasks with dynamic environments and complex interactions remains unexplored. To bridge the gap, we introduce WebDevJudge, a systematic benchmark for assessing LLM-as-a-judge performance in web development, with support for both non-interactive evaluation based on static observations and continuous interactive evaluation with a dynamic web environment. WebDevJudge comprises human preference labels over paired web implementations, annotated with structured and query-grounded rubrics to ensure high-quality ground truth. Using this benchmark, we comprehensively evaluate various evaluators, including LLMs, MLLMs, and agentic workflows. We systematically investigate the impact of different paradigms and guidance mechanisms. Our experiments reveal a significant gap between LLM judges and human experts. In-depth analysis indicates this gap stems from fundamental model limitations, including failures in recognizing functional equivalence, verifying task feasibility, and mitigating bias. Overall, WebDevJudge presents a significant challenge to LLM-as-a-judge, offering insights to guide future research toward developing more reliable and capable automated evaluators for complicated scenarios.
Massive Editing for Large Language Models Based on Dynamic Weight Generation
Authors: Wentao Wan; Qiqing Lao; Zhiwei Xie; Hefeng Wu; Runnan Lin; Liang Lin; Keze Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Knowledge Editing (KE) is a field that studies how to modify some knowledge in Large Language Models (LLMs) at a low cost (compared to pre-training). Currently, performing large-scale edits on LLMs while ensuring the Reliability, Generality, and Locality metrics of the edits remain a challenge. This paper proposes a Massive editing approach for LLMs based on dynamic weight Generation (MeG). Our MeG involves attaching a dynamic weight neuron to specific layers of the LLMs and using a diffusion model to conditionally generate the weights of this neuron based on the input query required for the knowledge. This allows the use of adding a single dynamic weight neuron to achieve the goal of large-scale knowledge editing. Experiments show that our MeG can significantly improve the performance of large-scale KE in terms of Reliability, Generality, and Locality metrics compared to existing knowledge editing methods, particularly with a high percentage point increase in the absolute value index for the Locality metric, demonstrating the advantages of our proposed method.
On the Convergence of Two-Layer Kolmogorov-Arnold Networks with First-Layer Training
Authors: Seyed Mohammad Eshtehardian; Mohammad Hossein Yassaee; Babak Khalaj
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Kolmogorov-Arnold Networks (KANs) have emerged as a promising alternative to traditional neural networks, offering enhanced interpretability based on the Kolmogorov-Arnold representation theorem. While their empirical success is growing, a theoretical understanding of their training dynamics remains nascent. This paper investigates the optimization of a two-layer KAN in the overparameterized regime, focusing on a simplified yet insightful setting where only the first-layer coefficients are trained via gradient descent.

Our main result establishes that, provided the network is sufficiently wide, this training method is guaranteed to converge to a global minimum and achieve zero training error. Furthermore, we derive a novel, fine-grained convergence rate that explicitly connects the optimization speed to the structure of the data labels through the eigenspectrum of the KAN Tangent Kernel (KAN-TK). Our analysis reveals a key advantage of this architecture: guaranteed convergence is achieved with a hidden layer width of $m=\mathcal{O}(n^2)$, a significant polynomial improvement over the $m=\mathcal{O}(n^6)$ requirement for classic two-layer neural networks using ReLU activation functions and analyzed within the same Tangent Kernel framework. We validate our theoretical findings with numerical experiments that corroborate our predictions on convergence speed and the impact of label structure.
Leveraging Discrete Function Decomposability for Scientific Design
Authors: James C Bowden; Sergey Levine; Jennifer Listgarten
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In the era of AI-driven science and engineering, we often want to design discrete
objects (e.g., circuits, proteins, materials) in silico according to user-specified
properties (e.g., that a protein binds its target). Given a property predictive model,
in silico design typically involves training a generative model over the design
space (e.g., over the set of all length-L proteins) to concentrate on designs with the
desired properties. Distributional optimization, formalized as an estimation of distribution algorithm or as reinforcement learning policy optimization, maximizes
an objective function in expectation over samples. Optimizing a distribution over
discrete-valued designs is in general challenging due to the combinatorial nature
of the design space. However, many property predictors in scientific applications
are decomposable in the sense that they can be factorized over design variables in a
way that will prove useful. For example, the active site amino acids in a catalytic
protein may need to only loosely interact with the rest of the protein for maximal catalytic activity. Current distributional optimization algorithms are unable to
make use of such structure, which could dramatically improve the optimization.
Herein, we propose and demonstrate use of a new distributional optimization algorithm, Decomposition-Aware Distributional Optimization (DADO),
that can leverage any decomposability defined by a junction tree on the design
variables. At its core, DADO employs a factorized “search distribution”—a
learned generative model—for efficient navigation of the search space, and invokes graph message passing to coordinate optimization across all variables.