GenAI4ELab/papercli-papers huggingface dataset:
Structural Inference: Interpreting Small Language Models with Susceptibilities
Authors: Garrett Baker; George Wang; Jesse Hoogland; Vinayak Pathak; Daniel Murfet
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We develop a linear response framework for interpretability that treats a neural network as a Bayesian statistical mechanical system. A small perturbation of the data distribution, for example shifting the Pile toward GitHub or legal text, induces a first-order change in the posterior expectation of an observable localized on a chosen component of the network. The resulting susceptibility can be estimated efficiently with local SGLD samples and factorizes into signed, per-token contributions that serve as attribution scores. We combine these susceptibilities into a response matrix whose low-rank structure separates functional modules such as multigram and induction heads in a 3M-parameter transformer.
Lipschitz Bandits with Stochastic Delayed Feedback
Authors: Zhongxuan Liu; Yue Kang; Thomas Lee
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The Lipschitz bandit problem extends stochastic bandits to a continuous action set defined over a metric space, where the expected reward function satisfies a Lipschitz condition. In this work, we introduce a new problem of Lipschitz bandit in the presence of stochastic delayed feedback, where the rewards are not observed immediately but after a random delay. We consider both bounded and unbounded stochastic delays, and design algorithms that attain sublinear regret guarantees in each setting. For bounded delays, we propose a delay-aware zooming algorithm that retains the optimal performance of the delay-free setting up to an additional term that scales with the maximum delay $\tau_{\max}$. For unbounded delays, we propose a novel phased learning strategy that accumulates reliable feedback over carefully scheduled intervals, and establish a regret lower bound showing that our method is nearly optimal up to logarithmic factors.
Finally, we present experimental results to demonstrate the efficiency of our algorithms under various delay scenarios.
Learning Facts at Scale with Active Reading
Authors: Jessy Lin; Vincent-Pierre Berges; Xilun Chen; Wen-tau Yih; Gargi Ghosh; Barlas Oguz
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: LLMs are known to store vast amounts of knowledge in their parametric memory.
However, learning and recalling facts from this memory is known to be unreliable, depending largely on the prevalence of particular facts in the training data and other factors which are poorly understood.
Practitioners are lacking tools which will allow them to ensure that the models learn a given body of knowledge reliably and consistently.
To this end, we propose Active Reading: a framework where we train models to study a given set of material with self-generated learning strategies.
First, we demonstrate models trained with Active Reading on expert domains absorb significantly more knowledge than vanilla finetuning and other data augmentations.
We train expert 8B models that achieve 66% on a Wikipedia-grounded subset of SimpleQA (+313% relative over vanilla finetuning) and 26% on FinanceBench (+160% relative over vanilla finetuning) by applying Active Reading to the source documents for each benchmark.
Finally, we show that Active Reading can be utilized at pre-training scale to build more factual models.
As a demonstration of this, we release WikiExpert-8B, a Wikipedia-expert model trained on 1 trillion generated tokens, which outcompetes models with hundreds of billions of parameters on factual QA.
RMAAT: Astrocyte-Inspired Memory Compression and Replay for Efficient Long-Context Transformers
Authors: Md Zesun Ahmed Mia; Malyaban Bal; Abhronil Sengupta
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The quadratic complexity of self-attention mechanism presents a significant impediment to applying Transformer models to long sequences. This work explores computational principles derived from astrocytes—glial cells critical for biological memory and synaptic modulation—as a complementary approach to conventional architectural modifications for efficient self-attention. We introduce the Recurrent Memory Augmented Astromorphic Transformer (RMAAT), an architecture integrating abstracted astrocyte functionalities. RMAAT employs a recurrent, segment-based processing strategy where persistent memory tokens propagate contextual information. An adaptive compression mechanism, governed by a novel retention factor derived from simulated astrocyte long-term plasticity (LTP), modulates these tokens. Attention within segments utilizes an efficient, linear-complexity mechanism inspired by astrocyte short-term plasticity (STP). Training is performed using Astrocytic Memory Replay Backpropagation (AMRB), a novel algorithm designed for memory efficiency in recurrent networks. Evaluations on the Long Range Arena (LRA) benchmark demonstrate RMAAT's competitive accuracy and substantial improvements in computational and memory efficiency, indicating the potential of incorporating astrocyte-inspired dynamics into scalable sequence models.
HWC-Loco: A Hierarchical Whole-Body Control Approach to Robust Humanoid Locomotion
Authors: Sixu Lin; Guanren Qiao; Yunxin Tai; Ang Li; Kui Jia; Guiliang Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Humanoid robots, capable of assuming human roles in various workplaces, have become essential to embodied intelligence. However, as robots with complex physical structures, learning a control model that can operate robustly across diverse environments remains inherently challenging, particularly under the discrepancies between training and deployment environments. In this study, we propose HWC-Loco, a robust whole-body control algorithm tailored for humanoid locomotion tasks. By reformulating policy learning as a robust optimization problem, HWC-Loco explicitly learns to recover from safety-critical scenarios. While prioritizing safety guarantees, overly conservative behavior can compromise the robot's ability to complete the given tasks. To tackle this challenge, HWC-Loco leverages a hierarchical policy for robust control. This policy can dynamically resolve the trade-off between goal-tracking and safety recovery, guided by human behavior norms and dynamic constraints. To evaluate the performance of HWC-Loco, we conduct extensive comparisons against state-of-the-art humanoid control models, demonstrating HWC-Loco's superior performance across diverse terrains, robot structures, and locomotion tasks under both simulated and real-world environments.
TrustGen: A Platform of Dynamic Benchmarking on the Trustworthiness of Generative Foundation Models
Authors: Yue Huang; Chujie Gao; Siyuan Wu; Haoran Wang; Xiangqi Wang; Jiayi Ye; Yujun Zhou; Yanbo Wang; Jiawen Shi; Qihui Zhang; Han Bao; Zhaoyi Liu; Yuan Li; Tianrui Guan; Peiran Wang; Haomin Zhuang; Dongping Chen; Kehan Guo; Andy Zou; Bryan Hooi; Caiming Xiong; Elias Stengel-Eskin; Hongyang Zhang; Hongzhi Yin; Huan Zhang; Huaxiu Yao; Jieyu Zhang; Jaehong Yoon; Kai Shu; Ranjay Krishna; Swabha Swayamdipta; Weijia Shi; Xiang Li; Yuexing Hao; Zhihao Jia; Zhize Li; Xiuying Chen; Zhengzhong Tu; Xiyang Hu; Tianyi Zhou; Jieyu Zhao; Lichao Sun; Furong Huang; Or Cohen-Sasson; Prasanna Sattigeri; Anka Reuel; Max Lamparth; Yue Zhao; Nouha Dziri; Yu Su; Huan Sun; Heng Ji; Chaowei Xiao; Mohit Bansal; Nitesh V Chawla; Jian Pei; Jianfeng Gao; Michael Backes; Philip S. Yu; Neil Zhenqiang Gong; Pin-Yu Chen; Bo Li; Dawn Song; Xiangliang Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Generative foundation models (GenFMs), such as large language models and text-to-image systems, have demonstrated remarkable capabilities in various downstream applications. As they are increasingly deployed in high-stakes applications, assessing their trustworthiness has become both a critical necessity and a substantial challenge. Existing evaluation efforts are fragmented, rapidly outdated, and often lack extensibility across modalities. This raises a fundamental question: how can we systematically, reliably, and continuously assess the trustworthiness of rapidly advancing GenFMs across diverse modalities and use cases? To address these gaps, we introduce TrustGen, a dynamic and modular benchmarking system designed to systematically evaluate the trustworthiness of GenFMs across text-to-image, large language, and vision-language modalities. TrustGen standardizes trust evaluation through a unified taxonomy of over 25 fine-grained dimensions—including truthfulness, safety, fairness, robustness, privacy, and machine ethics—while supporting dynamic data generation and adaptive evaluation through three core modules: Metadata Curator, Test Case Builder, and Contextual Variator. Taking TrustGen into action to evaluate the trustworthiness of 39 models reveals four key insights. (1) State-of-the-art GenFMs achieve promising overall trust performance, yet significant limitations remain in specific dimensions such as hallucination resistance, fairness, and privacy preservation. (2) Contrary to prevailing assumptions, open-source models now rival and occasionally surpass proprietary systems in trustworthiness metrics. (3) The trust gap among top-performing models is narrowing, likely due to increased industry convergence on best practices. (4) Trustworthiness is not an isolated property; it interacts complexly with other behaviors, such as helpfulness and ethical decision-making. TrustGen is a transformative step toward standardized, scalable, and actionable trustworthiness evaluation, supporting dynamic assessments across diverse modalities and trust dimensions that evolve alongside the generative AI landscape.
SPIKE-RL: Video-LLMs meet Bayesian Surprise
Authors: Sahithya Ravi; Aditya Chinchure; Raymond T. Ng; Leonid Sigal; Vered Shwartz
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Real-world videos often show routine activities punctuated by memorable, surprising events. However, most Video-LLMs process videos by sampling frames uniformly, likely missing critical moments that define a video's narrative. We introduce SPIKE, an inference-time framework that quantifies Bayesian Surprise as the belief update triggered by new visual evidence in the video stream, identifying moments where new visual evidence conflicts with prior beliefs. SPIKE effectively localizes surprise in videos, correlated with humans on positive (FunQA) and negative (Oops!) surprise benchmarks. SPIKE-RL further improves on SPIKE's ability to detect surprise, leveraging GRPO to refine its belief hypotheses based on a reward signal from the video caption. SPIKE and SPIKE-RL guide query-agnostic surprise-weighted frame sampling, which allocates more frames to interesting moments in the video. With this strategy, we achieve consistent performance gains on five downstream benchmarks. By enabling Video-LLMs to track beliefs and register surprise, our work paves the way for more robust models that can revise their understanding in response to new information.
BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for  Digital  Behavioural Change
Authors: Manuela González-González; Soufiane Belharbi; Muhammad Osama Zeeshan; Masoumeh Sharafi; Muhammad Haseeb Aslam; Marco Pedersoli; Alessandro Lameiras Koerich; Simon L Bacon; Eric Granger
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Ambivalence and hesitancy (A/H), closely related constructs, are the primary reasons why individuals delay, avoid, or abandon health behaviour changes. They are subtle and conflicting emotions that sets a person in a state between positive and negative orientations, or between acceptance and refusal to do something. They manifest as a discord in affect between multiple modalities or within a modality, such as facial and vocal expressions, and body language. 
Although experts can be trained to recognize A/H as done for in-person interactions, integrating them into digital health interventions is costly and less effective. Automatic A/H recognition is therefore critical for the personalization and cost-effectiveness of digital behaviour change interventions. However, no datasets currently exist for the design of machine learning models to recognize A/H. 
This paper introduces the Behavioural Ambivalence/Hesitancy (BAH) dataset collected for multimodal recognition of A/H in videos. It contains 1,427 videos with a total duration of 10.60 hours, captured from 300 participants across Canada, answering predefined questions to elicit A/H.  
It is intended to mirror real-world digital behaviour change interventions delivered online. BAH is annotated by three experts to provide timestamps that indicate where A/H occurs, and frame- and video-level annotations with A/H cues. Video transcripts,  
cropped and aligned faces, and participant metadata are also provided. Since A and H manifest similarly in practice, we provide a binary annotation indicating the presence or absence of A/H.
Additionally, this paper includes benchmarking results using baseline models on BAH for frame- and video-level recognition, zero-shot prediction, and personalization with source-free domain adaptation methods. The limited performance highlights the need for adapted multimodal and spatio-temporal models for A/H recognition. Results obtained with specialized fusion methods are shown to assess the presence of conflicts between modalities, additionally temporal modelling for within-modality conflicts are essential for more discriminant A/H recognition.
The data, code, and pretrained weights are publicly available: https://github.com/LIVIAETS/bah-dataset.
On the Interpolation Effect of Score Smoothing in Diffusion Models
Authors: Zhengdao Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion models have achieved remarkable progress in various domains with an intriguing ability to produce new data that do not exist in the training set. In this work, we study the hypothesis that such creativity arises from the neural network backbone learning a smoothed version of the empirical score function, which guides the denoising dynamics to generate data points that interpolate the training data. Focusing mainly on settings where the training set lies uniformly in a one-dimensional subspace, we elucidate the interplay between score smoothing and the denoising dynamics with analytical solutions and numerical experiments, demonstrating how smoothing the score function can cause the denoised data samples to interpolate the training set along the subspace. Moreover, we present theoretical and empirical evidence that learning score functions with neural networks - either with or without explicit regularization - can naturally achieve a similar effect, including when the data belong to simple nonlinear manifolds.
Bottlenecked Transformers: Periodic KV Cache Consolidation for Generalised Reasoning
Authors: Adnan Oomerjee; Zafeirios Fountas; Haitham Bou Ammar; Jun Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Transformer LLMs have been shown to exhibit strong reasoning ability that scales with inference-time compute, most prominently through token-space “thinking” (i.e., chains of thought). A growing line of work pushes this extra computation into the model’s latent space (adjacent to standard decoding) which we term Auxiliary Latent-Space Computation (ALSC). Existing ALSC methods largely fall into three buckets: (i) token-mediated latent or special-token rollouts, (ii) residual/activation steering, and (iii) memory compression via cache pruning, merging, or summarization. An underexplored alternative is memory consolidation and reconsolidation, two processes in the brain that are responsible for stabilising newly formed memory traces, and, upon recall, transiently rendering established traces plastic such they can integrate new contextual information before restabilising. In a Transformer LLM, this can be seen as analogous to performing in-place global rewrites of incoming KV segments, and rewrites of past segments conditioned on newly observed tokens. In this work, we give a theoretical justification as to why memory (re)consolidation via KV cache rewrites is beneficial for improved reasoning. We do this through the lens of Information Bottleneck (IB) theory, which posits that model generalisation emerges from an optimal balance between input information compression and retention of predictive information in latent representations. We prove using IB theory that Vanilla decoder-only Transformers are inherently constrained in their ability to form task-optimal sequence representations. We then introduce the Bottlenecked Transformer, which augments a decoder-only backbone LLM with a lightweight Cache Processor, an auxiliary Transformer that performs periodic, non-causal, in-place KV rewrites at newline-delimited reasoning step boundaries. The processor consolidates recently written KV entries and reconsolidates a small, top-$k$ attention-selected set of prior entries, conditioned on recent context. We evaluate our Bottlenecked Transformer architecture on seven mathematical reasoning benchmarks, with four backbone LLMs. Our model sees consistent performance gains over vanilla Transformers and pause-token augmented Transformer baselines, with gains of up to +6.6pp for selected tasks and backbones.
Signal in the Noise: Polysemantic Interference Transfers and Predicts Cross-Model Influence
Authors: Bofan Gong; Shiyang Lai; James Evans; Dawn Song
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Polysemanticity is pervasive in language models and remains a major challenge for interpretation and model behavioral control. Leveraging sparse autoencoders (SAEs), we map the polysemantic topology of two small models (Pythia-70M and GPT-2-Small) to identify SAE feature pairs that are semantically unrelated yet exhibit interference within models. We intervene at four loci (prompt, token, feature, neuron) and measure induced shifts in the next-token prediction distribution, uncovering polysemantic structures that expose a systematic vulnerability in these models. Critically, interventions distilled from counterintuitive interference patterns shared by two small models transfer reliably to larger instruction-tuned models (Llama-3.1-8B/70B-Instruct and Gemma-2-9B-Instruct), yielding predictable behavioral shifts without access to model internals. These findings challenge the view that polysemanticity is purely stochastic, demonstrating instead that interference structures generalize across scale and family. Such generalization suggests a convergent, higher-order organization of internal representations, which is only weakly aligned with intuition and structured by latent regularities, offering new possibilities for both black-box control and theoretical insight into human and artificial cognition.
AdaCache: Adaptive Caching and Context Augmentation for Efficient LLM Serving
Authors: Zeng Zihao; Siyi Li; Xinyu Yan; Lei Xiao; Wei Yang Bryan Lim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Retrieval-Augmented Generation (RAG) significantly enhances Large Language Models by integrating external knowledge sources, but at the cost of substantial computational overhead from extended input sequences. 
Current RAG systems exhibit two fundamental inefficiencies: redundant processing of frequently retrieved text chunks across multiple queries, and uniform deep retrieval that over-provisions context regardless of query complexity.
We present AdaCache, an adaptive caching framework that addresses these limitations through dual optimization strategies. 
First, we introduce a cache-aware partial recomputation mechanism that profiles attention patterns to construct selective cache variants, enabling flexible reuse while preserving cross-chunk dependencies. 
Second, we develop adaptive context augmentation that dynamically determines optimal retrieval depth via lightweight confidence estimation, avoiding unnecessary overhead on simple queries.
Comprehensive experiments across diverse datasets and LLMs demonstrate that AdaCache delivers substantial improvements in Time-To-First-Token compared to state-of-the-art RAG caching systems, while preserving generation quality.
Code World Models for General Game Playing
Authors: Wolfgang Lehrach; Daniel Hennes; Miguel Lazaro-Gredilla; Xinghua Lou; Carter Wendelken; Zun Li; Antoine Dedieu; Marc Lanctot; Atil Iscen; John Schultz; Marcus Chiam; Ian Gemp; Piotr Zielinski; Satinder Singh; Kevin Patrick Murphy
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) reasoning abilities are increasingly being applied to classical board and card games, but the dominant approach---involving prompting for direct move generation---has significant drawbacks. It relies on the model's implicit fragile pattern-matching capabilities, leading to frequent illegal moves and strategically shallow play. Here we introduce an alternative approach: We use the LLM to translate natural language rules and game trajectories into a formal, executable world model  represented as Python code. This generated model---comprising functions for state transition, legal move enumeration, and termination checks---serves as a verifiable simulation engine for high-performance planning algorithms like Monte Carlo tree search (MCTS). In addition, we prompt the LLM to generate heuristic value functions (to make MCTS more efficient), and inference functions (to estimate hidden states in imperfect information games). Our method offers three distinct advantages compared to directly using the LLM as a policy: (1) Verifiability: The generated CWM serves as a formal specification of the game's rules, allowing planners to algorithmically enumerate valid actions and avoid illegal moves, contingent on the correctness of the synthesized model; (2) Strategic Depth: We combine LLM semantic understanding with the deep search power of classical planners; and (3) Generalization: We direct the LLM to focus on the meta-task of data-to-code translation, enabling it to adapt to new games more easily. We evaluate our agent on 10 different games,  of which 4 are novel and created for this paper. 5 of the games are  fully observed (perfect information), and 5 are partially observed (imperfect information). We find that our method outperforms or matches Gemini 2.5 Pro in 9 out of the 10 considered games.
To Compress or Not? Pushing the Frontier of Lossless GenAI Model Weights Compression with Exponent Concentration
Authors: Zeyu Yang; Tianyi Zhang; Jianwen Xie; Chuan Li; Zhaozhuo Xu; Anshumali Shrivastava
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The scaling of Generative AI (GenAI) models into the hundreds of billions of parameters makes low-precision computation indispensable for efficient deployment. We argue that the fundamental solution lies in developing low-precision \emph{floating-point} formats, which inherently provide numerical stability, memory savings, and hardware efficiency without dequantization overhead. In this paper, we present a theoretical and empirical study of an \emph{exponent concentration} phenomenon in GenAI weights: exponents consistently exhibit low entropy across architectures and modalities. We show that this arises naturally from $\alpha$-stable distributions induced by stochastic gradient descent, and we prove tight bounds on the entropy of exponents. Our analysis establishes a theoretical compression limit near FP4.67, which motivates the design of a practical FP8 format. Building on these insights, we propose Exponent-Concentrated FP8 (ECF8), a lossless compression framework with entropy-aware encoding and GPU-optimized decoding. Experiments on LLMs and DiTs up to 671B parameters demonstrate up to 26.9\% memory savings and 177.1\% throughput acceleration, with perfectly lossless computations, i.e., no deviation in model outputs. Our results establish exponent concentration as a statistical law of trained models and open a principled path for lossless low-precision floating-point design in the FP8 era. Code is available at https://github.com/zeyuyang8/ecf8.