GenAI4ELab/papercli-papers huggingface dataset:
On Measuring Influence in Avoiding Undesired Future
Authors: Lue Tao; Tian-Zuo Wang; Yuan Jiang; Zhi-Hua Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: When a predictive model anticipates an undesired future event, a question arises: What can we do to avoid it? Resolving this forward-looking challenge requires determining the variables that positively influence the future, moving beyond the statistical *association* typically exploited for prediction. In this paper, we introduce a novel measure for evaluating the *influence* of actionable variables in successfully avoiding the undesired future. We quantify influence as the degree to which the success probability can be increased by altering variables under the principle of maximum expected utility. Our analysis demonstrates a counterintuitive insight: while related to *causality*, influential variables may not necessarily be those with strong intrinsic causal effects on the target event. In fact, it can be highly beneficial to alter a weak causal factor, or even a variable that is not an intrinsic factor at all. We provide a practical implementation for estimating the proposed measure and validate its utility through experiments on synthetic and real-world tasks.
Peng's Q($\lambda$) for Conservative Value Estimation in Offline Reinforcement Learning
Authors: Byeongchan Kim; Min-hwan Oh
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose a model-free offline multi-step reinforcement learning (RL) algorithm, Conservative Peng's Q($\lambda$) (CPQL). Our algorithm adapts the Peng's Q($\lambda$) (PQL) operator for conservative value estimation as an alternative to the Bellman operator. To the best of our knowledge, this is the first work in offline RL to theoretically and empirically demonstrate the effectiveness of conservative value estimation with a *multi-step* operator by fully leveraging offline trajectories. The fixed point of the PQL operator in offline RL lies closer to the value function of the behavior policy, thereby naturally inducing implicit behavior regularization. CPQL simultaneously mitigates over-pessimistic value estimation, achieves performance greater than (or equal to) that of the behavior policy, and provides near-optimal performance guarantees --- a milestone that previous conservative approaches could not achieve. Extensive numerical experiments on the D4RL benchmark demonstrate that CPQL consistently and significantly outperforms existing offline single-step baselines. In addition to the contributions of CPQL in offline RL, our proposed method also contributes to the offline-to-online learning framework. Using the Q-function pre-trained by CPQL in offline settings enables the online PQL agent to avoid the performance drop typically observed at the start of fine-tuning and to attain robust performance improvements. Our code is available at https://github.com/oh-lab/CPQL.
UIS-Digger: Towards Comprehensive Research Agent Systems for Real-world Unindexed Information Seeking
Authors: Chang Liu; Chuqiao Kuang; Tianyi Zhuang; Yuxin Cheng; Huichi Zhou; Xiaoguang Li; Lifeng Shang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advancements in LLM-based information-seeking agents have achieved record-breaking performance on established benchmarks. However, these agents remain heavily reliant on search-engine-indexed knowledge, leaving a critical blind spot: Unindexed Information Seeking (UIS). This paper identifies and explores the UIS problem, where vital information is not captured by search engine crawlers, such as overlooked content, dynamic webpages, and embedded files. Despite its significance, UIS remains an underexplored challenge. To address this gap, we introduce UIS-QA, the first dedicated UIS benchmark, comprising 110 expert-annotated QA pairs. Notably, even state-of-the-art agents experience a drastic performance drop on UIS-QA (e.g., from 70.90 on GAIA and 46.70 on BrowseComp-zh to 24.55 on UIS-QA), underscoring the severity of the problem. To mitigate this, we propose UIS-Digger, a novel multi-agent framework that incorporates dual-mode browsing and enables simultaneous webpage searching and file parsing. With a relatively small $\sim$30B-parameter backbone LLM optimized using SFT and RFT training strategies, UIS-Digger sets a strong baseline at 26.36\%, outperforming systems integrating sophisticated LLMs such as O3 and GPT-4.1. This demonstrates the importance of proactive interaction with unindexed sources for effective and comprehensive information-seeking. Our work not only uncovers a fundamental limitation in current agent evaluation paradigms but also provides the first toolkit for advancing UIS research, defining a new and promising direction for robust information-seeking systems.
Towards Understanding the Shape of Representations in Protein Language Models
Authors: Kosio Beshkov; Anders Malthe-Sorenssen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While protein language models (PLMs) are one of the most promising avenues of research for future de novo protein design, the way in which they transform sequences to hidden representations, as well as the information encoded in such representations is yet to be fully understood. Several works have attempted to propose interpretability tools for PLMs, but they have focused on understanding how individual sequences are transformed by such models. Therefore, the way in which PLMs transform the whole space of sequences along with their relations is still unknown. In this work we attempt to understand this transformed space of sequences by identifying protein structure and representation with square-root velocity (SRV) representations and graph filtrations. Both approaches naturally lead to a metric space in which pairs of proteins or protein representations can be compared with each other.

We analyze different types of proteins from the SCOP dataset and show that the Fréchet radius and effective dimension of the SRV shape space follows a non-linear pattern as a function of the layers in ESM2 models of different sizes. Furthermore, we use graph filtrations as a tool to study the context lengths at which models encode the structural features of proteins. We find that PLMs preferentially encode immediate as well as local relations between amino acids, but start to degrade for larger context lengths. The most structurally faithful encoding tends to occur close to, but before the last layer of the models, indicating that training a folding model ontop of these layers might lead to improved folding performance.
Inference-Cost-Aware Dynamic Tree Construction for Efficient Inference in Large Language Models
Authors: Yinrong Hong; Zhiquan Tan; Kai Hu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) face significant inference latency challenges stemming from their autoregressive design and large size. To address this, speculative decoding emerges as a solution, enabling the simultaneous generation and validation of multiple tokens. While recent approaches like EAGLE-2 and EAGLE-3 improve speculative decoding using dynamic tree structures, they often neglect the impact of crucial system variables such as GPU devices and batch sizes.

Therefore, we introduce a new dynamic tree decoding approach called CAST that takes into account inference costs, including factors such as GPU configurations and batch sizes, to dynamically refine the tree structure. Through comprehensive experimentation across six diverse tasks and utilizing six distinct LLMs, our methodology demonstrates remarkable results, achieving speeds up to 5.2 times faster than conventional decoding methods. Moreover, it generally outperforms existing state-of-the-art techniques from $5\%$ to $20\%$. The code is available at \url{https://github.com/EAGLE-Research/sglang-eagle4}.
SparseEval: Efficient Evaluation of Large Language Models by Sparse Optimization
Authors: Taolin Zhang; Hang Guo; Wang Lu; Tao Dai; Shu-Tao Xia; Jindong Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: As large language models (LLMs) continue to scale up, their performance on various downstream tasks has significantly improved. However, evaluating their capabilities has become increasingly expensive, as performing inference on a large number of benchmark samples incurs high computational costs.
In this paper, we revisit the model-item performance matrix and show that it exhibits sparsity, that representative items can be selected as anchors, and that the task of efficient benchmarking can be formulated as a sparse optimization problem. 
Based on these insights, we propose SparseEval, a method that, for the first time, adopts gradient descent to optimize anchor weights and employs an iterative refinement strategy for anchor selection. We utilize the representation capacity of MLP to handle sparse optimization and propose the Anchor Importance Score and Candidate Importance Score to evaluate the value of each item for task-aware refinement. Extensive experiments demonstrate the low estimation error and high Kendall’s $\tau$ of our method across a variety of benchmarks, showcasing its superior robustness and practicality in real-world scenarios. Code is available at https://github.com/taolinzhang/SparseEval.
Propaganda AI: An Analysis of Semantic Divergence in Large Language Models
Authors: Nay Myat Min; Long H. Pham; Yige Li; Jun Sun
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) can exhibit *concept-conditioned semantic divergence*: common high-level cues (e.g., ideologies, public figures) elicit unusually uniform, stance-like responses that evade token-trigger audits. This behavior falls in a blind spot of current safety evaluations, yet carries major societal stakes, as such concept cues can steer content exposure at scale. We formalize this phenomenon and present **RAVEN** (**R**esponse **A**nomaly **V**igilance), a black-box audit that flags cases where a model is simultaneously highly certain and atypical among peers by coupling *semantic entropy* over paraphrastic samples with *cross-model disagreement*. In a controlled LoRA fine-tuning study, we implant a concept-conditioned stance using a small biased corpus, demonstrating feasibility without rare token triggers. Auditing five LLM families across twelve sensitive topics (360 prompts per model) and clustering via bidirectional entailment, RAVEN surfaces recurrent, model-specific divergences in 9/12 topics. Concept-level audits complement token-level defenses and provide a practical early-warning signal for release evaluation and post-deployment monitoring against propaganda-like influence.
It's All Just Vectorization: einx, a Universal Notation for Tensor Operations
Authors: Florian Fervers; Sebastian Bullinger; Christoph Bodensteiner; Michael Arens
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Tensor operations represent a cornerstone of modern scientific computing. However, the Numpy-like notation adopted by predominant tensor frameworks is often difficult to read and write and prone to so-called shape errors, i.a., due to following inconsistent rules across a large, complex collection of operations. Alternatives like einsum and einops have gained popularity, but are inherently restricted to few operations and lack the generality required for a universal model of tensor programming.

To derive a better paradigm, we revisit vectorization as a function for transforming tensor operations, and use it to both lift lower-order operations to higher-order operations, and conceptually decompose higher-order operations to lower-order operations and their vectorization.

Building on the universal nature of vectorization, we introduce einx, a universal notation for tensor operations. It uses declarative, pointful expressions that are defined by analogy with loop notation and represent the vectorization of tensor operations. The notation reduces the large APIs of existing frameworks to a small set of elementary operations, applies consistent rules across all operations, and enables a clean, readable and writable representation in code. We provide an implementation of einx that is embedded in Python and integrates seamlessly with existing tensor frameworks: https://github.com/fferflo/einx
ATGen: Adversarial Reinforcement Learning for Test Case Generation
Authors: Qingyao Li; Xinyi Dai; Weiwen Liu; Xiangyang Li; Yasheng Wang; Ruiming Tang; Yong Yu; Weinan Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) excel at code generation, yet their outputs often contain subtle bugs, for which effective test cases are a critical bottleneck. Existing test generation methods, whether based on prompting or supervised fine-tuning, rely on static datasets. This imposes a “fixed-difficulty ceiling”, fundamentally limiting their ability to uncover novel or more complex bugs beyond their training scope. To overcome this, we introduce ATGEN, a framework that trains a test case generator via adversarial reinforcement learning. ATGEN pits a test generator against an adversarial code generator that continuously crafts harder bugs to evade the current policy. This dynamic loop creates a curriculum of increasing difficulty that continuously challenges the current policy. The test generator is optimized via Reinforcement Learning (RL) to jointly maximize “Output Accuracy” and “Attack Success”, enabling it to learn a progressively stronger policy that breaks the fixed-difficulty ceiling of static training. Extensive experiments demonstrate that ATGEN significantly outperforms state-of-the-art baselines. We further validate its practical utility, showing it serves as both a more effective filter for Best-of-N inference and a higher-quality reward source for training code generation models. Our work establishes a new, dynamic paradigm for improving the reliability of LLM-generated code.
ReactID: Synchronizing Realistic Actions and Identity in Personalized Video Generation
Authors: Wei Li; Yiheng Zhang; Fuchen Long; Zhaofan Qiu; Ting Yao; Xiaoyan Sun; Tao Mei
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Personalized video generation faces a fundamental trade-off between identity consistency and action realism: overly rigid identity preservation often leads to unnatural motion, while emphasis on action dynamics can compromise subject fidelity. This tension stems from three interrelated challenges: imprecise subject-video alignment, unstable training due to varying sample difficulties, and inadequate modeling of fine-grained actions. To address this, we propose ReactID, a comprehensive framework that harmonizes identity accuracy and motion naturalness through coordinated advances in data, training, and action modeling. First, we construct ReactID-Data, a large-scale dataset annotated with a high-precision pipeline combining vision-based entity label extraction, MLLM-based subject detection, and post-verification to ensure reliable subject-video correspondence. Second, we analyze learning difficulty along dimensions such as subject size, appearance similarity, and sampling strategy, and devise a progressive training curriculum that evolves from easy to hard samples, ensuring stable convergence while avoiding identity overfitting and copy-paste artifacts. Third, ReactID introduces a novel timeline-based conditioning mechanism that supplements monolithic text prompts with structured multi-action sequences. Each sub-action is annotated with precise timestamps and descriptions, and integrated into the diffusion model via two novel components: subject-aware cross-attention module to bind sub-action to the specific subject of interest and temporally-adaptive RoPE to embed the rescaled temporal coordinates invariant to action duration. Experiments show that ReactID achieves state-of-the-art performance in both identity preservation and action realism, effectively balancing the two objectives.
Metis: Training LLMs with FP4 Quantization
Authors: Hengjie Cao; Mengyi Chen; Yifeng Yang; Fang Dong; Ruijun Huang; Jixian Zhou; Anrui Chen; Mingzhi Dong; Yujiang Wang; Jinlong Hou; Yuan Cheng; FAN WU; Fan Yang; Tun Lu; Ning Gu; Li Shang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This work identifies anisotropy in the singular value spectra of parameters, activations, and gradients as the fundamental barrier to low-bit training of large language models (LLMs). These spectra are dominated by a small fraction of large singular values, inducing wide numerical ranges that cause quantization bias and severe spectral distortion, ultimately degrading training performance. This work presents \emph{Metis}, a spectral-domain quantization framework that partitions anisotropic spectra into narrower sub-distributions for independent quantization, thereby reducing errors and preserving spectral structure. To minimize overhead, 
Metis leverages two key properties of the dominant spectral subspace: preservation via sparsely random sampling and preservation via random projection, reducing decomposition cost to a negligible level. On LLaMA-3 8B trained with 100B tokens, Metis enables robust W4A4G4 training with FP4 quantization of weights, activations, and gradients, yielding only a 0.4\% training loss gap and a 0.1\% degradation in downstream accuracy relative to BF16. Beyond matching BF16 fidelity, Metis also surpasses Nvidia’s FP4 recipe, consistently achieving lower loss and higher downstream accuracy while incurring significantly lower computational overhead. 
The code implementation for Metis is available at: \url{https://github.com/sii-research/Metis}.
Hedonic Neurons: A Mechanistic Mapping of Latent Coalitions in Transformer MLPs
Authors: Tanya Chowdhury; Atharva Nijasure; Yair Zick; James Allan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Fine-tuned Large Language Models (LLMs) encode rich task-specific features, but the form of these representations—especially within MLP layers—remains unclear. Empirical inspection of LoRA updates shows that new features concentrate in mid-layer MLPs, yet the scale of these layers obscures meaningful structure. Prior probing suggests that statistical priors may strengthen, split, or vanish across depth, motivating the need to study how neurons work together rather than in isolation.  

We introduce a mechanistic interpretability framework based on coalitional game theory, where neurons mimic agents in a hedonic game whose preferences capture their synergistic contributions to layer-local computations. Using top-responsive utilities and the PAC-Top-Cover algorithm, we extract stable coalitions of neurons—groups whose joint ablation has non-additive effects—and track their transitions across layers as persistence, splitting, merging, or disappearance.  

Applied to LLaMA, Mistral, and Pythia rerankers fine-tuned on scalar output tasks, our method finds coalitions with consistently higher synergy than clustering baselines. By revealing how neurons cooperate to encode features, hedonic coalitions uncover higher-order structure beyond disentanglement and yield computational units that are functionally important, interpretable, and predictive across domains.
Learning Physics-Grounded 4D Dynamics with Neural Gaussian Force Fields
Authors: Shiqian Li; Ruihong Shen; Junfeng Ni; Chang Pan; Chi Zhang; Yixin Zhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Predicting physical dynamics from raw visual data remains a major challenge in AI. While recent video generation models have achieved impressive visual quality, they still cannot consistently generate physically plausible videos due to a lack of modeling of physical laws. Recent approaches combining 3D Gaussian splatting and physics engines can produce physically plausible videos, but are hindered by high computational costs in both reconstruction and simulation, and often lack robustness in complex real-world scenarios. To address these issues, we introduce **Neural Gaussian Force Field (NGFF)**, an end-to-end neural framework that integrates 3D Gaussian perception with physics-based dynamic modeling to generate interactive, physically realistic 4D videos from multi-view RGB inputs, achieving two orders of magnitude faster than prior Gaussian simulators. To support training, we also present **GSCollision**, a 4D Gaussian dataset featuring diverse materials, multi-object interactions, and complex scenes, totaling over 640k rendered physical videos (∼4 TB). Evaluations on synthetic and real 3D scenarios show NGFF’s strong generalization and robustness in physical reasoning, advancing video prediction towards physics-grounded world models.
Soft-Masked Diffusion Language Models
Authors: Michael Hersche; Samuel Moor-Smith; Thomas Hofmann; Abbas Rahimi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion models have demonstrated strong potential in language modeling, offering various advantages over traditional autoregressive approaches. Their ability to generate and revise entire responses in parallel enables faster generation and built-in self-correction mechanisms. Most modern diffusion-based language models employ masked diffusion, where decoding involves iteratively processing masked tokens based on a binary decision: either retaining the mask or replacing it with the predicted token. However, this binary choice discards valuable predictive information when the mask is retained. To address this limitation, we introduce soft-masking (SM), a novel method that dynamically blends the embedding of the mask token with the embeddings of the top-k predicted tokens from the previous decoding step, for each retained mask. This provides the model with a more informative prior, preserving context from earlier computations and allowing partial information about masked tokens to propagate beyond a single step. We propose a training methodology that efficiently adapts masked diffusion language models to incorporate SM. We demonstrate that training a 169M parameter model from scratch with SM yields superior perplexity and MAUVE scores compared to binary masking baselines. Similarly, a pretrained model can be enhanced with SM through continued pretraining. Finally, we finetune two state-of-the-art diffusion models, Dream-7B and Dream-Coder-7B, with SM. SM consistently improves performance across multiple coding benchmarks, particularly in high-throughput settings.
MSCR: Exploring the Vulnerability of LLMs’ Mathematical Reasoning Abilities Using Multi-Source Candidate Replacement
Authors: Zhishen Sun; Guang Dai; Haishan Ye
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: LLMs demonstrate performance comparable to human abilities in complex tasks such as mathematical reasoning, but their robustness in mathematical reasoning under minor input perturbations still lacks systematic investigation. Existing methods generally suffer from limited scalability, weak semantic preservation, and high costs. Therefore, we propose MSCR, an automated adversarial attack method based on multi-source candidate replacement. By combining three information sources including cosine similarity in the embedding space of LLMs, the WordNet dictionary, and contextual predictions from a masked language model, we generate for each word in the input question a set of semantically similar candidates, which are then filtered and substituted one by one to carry out the attack. We conduct large-scale experiments on LLMs using the GSM8K and MATH500 benchmarks. The results show that even a slight perturbation involving only a single word can significantly reduce the accuracy of all models, with the maximum drop reaching 49.89\% on GSM8K and 35.40\% on MATH500, _while preserving the high semantic consistency of the perturbed questions._ Further analysis reveals that perturbations not only lead to incorrect outputs but also substantially increase the average response length, which results in more redundant reasoning paths and higher computational resource consumption. These findings highlight the robustness deficiencies and efficiency bottlenecks of current LLMs in mathematical reasoning tasks.