GenAI4ELab/papercli-papers huggingface dataset:
Every Language Model Has a Forgery-Resistant Signature
Authors: Matthew Finlayson; Xiang Ren; Swabha Swayamdipta
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: The ubiquity of closed-weight language models with public-facing APIs has generated interest in forensic methods, both for extracting hidden model details (e.g., parameters) and identifying models by their outputs. One successful approach to these goals has been to exploit the geometric constraints imposed by the language model architecture and parameters. In this work, we show that a lesser-known geometric constraint—namely that language model outputs lie on the surface of a high-dimensional ellipse—functions as a signature for the model, which be used to identify which model an output came from. This ellipse signature has unique properties that distinguish it from existing model-output association methods like language model watermarks. In particular, the signature is hard to forge: without direct access to model parameters, it is practically infeasible to produce logprobs on the ellipse. Secondly, the signature is naturally occurring, since all language models have these elliptical constraints. Thirdly, the signature is self-contained, in that it is detectable without access to the model input or full weights. Finally, the signature is exceptionally redundant, as it is independently detectable in every single logprob output from the model. We evaluate a novel technique for extracting the ellipse on small models, and discuss the practical hurdles that make it infeasible for production-size models, making the signature hard to forge. Finally, we use ellipse signatures to propose a protocol for language model output verification, which is analogous to cryptographic symmetric-key message authentication systems.
WARP: Weight Teleportation for Attack-Resilient Unlearning Protocols
Authors: Mohammad mahdi Maheri; Xavier Cadet; Peter Chin; Hamed Haddadi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Approximate machine unlearning aims to efficiently remove the influence of specific data points from a trained model, offering a practical alternative to full retraining. However, it introduces privacy risks: an adversary with access to both the original and unlearned models can exploit their differences for membership inference or data reconstruction. We show these vulnerabilities arise from two factors: large gradient norms of forgotten samples and the close proximity of the unlearned model to the original. To demonstrate their severity, we design unlearning-specific membership inference and reconstruction attacks, showing that several state-of-the-art methods (such as NGP and SCRUB) remain vulnerable.

To mitigate this leakage, we introduce WARP, a plug-and-play teleportation defense that leverages neural network symmetries to reduce gradient energy of forgotten samples and increase parameter dispersion while preserving accuracy. This reparameterization hides the signal of forgotten data, making it harder for attackers to distinguish forgotten samples from non-members or to recover them through reconstruction. Across six unlearning algorithms, our approach achieves consistent privacy gains, reducing adversarial advantage by up to 64% in black-box settings and 92% in white-box settings, while maintaining accuracy on retained data. These results highlight teleportation as a general tool for improving privacy in approximate unlearning.
STEM: SCALING TRANSFORMERS WITH EMBEDDING MODULES
Authors: Ranajoy Sadhukhan; Sheng Cao; Harry Dong; Changsheng Zhao; Attiano Purpura-Pontoniere; Yuandong Tian; Zechun Liu; Beidi Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Fine-grained sparsity promises higher parametric capacity without proportional per-token compute, but often suffers from training instability, load balancing, and communication overhead. We introduce \textbf{STEM} (\emph{Scaling Transformers with Embedding Modules}), a static, token-indexed approach that replaces the FFN up-projection with a layer-local embedding lookup while keeping the gate and down-projection dense. This removes runtime routing, enables CPU offload with asynchronous prefetch, and decouples capacity from both per-token FLOPs and cross-device communication. Empirically, STEM trains stably despite extreme sparsity. It improves downstream performance over dense baselines while reducing per-token FLOPs and parameter accesses (eliminating roughly one-third of FFN parameters). STEM learns embedding spaces with large angular spread which enhances it knowledge storage capacity. In addition, STEM strengthens long-context performance: as sequence length grows, more distinct parameters are activated, yielding practical test-time capacity scaling. Across 350M and 1B model scales, STEM delivers up to $\sim$3--4\% improvements in average downstream performance, with notable gains on knowledge and reasoning-heavy benchmarks (ARC-Challenge, OpenBookQA, GSM8K, MMLU). Overall, STEM is an effective way of scaling parametric memory while remaining simpler to train and deploy than existing fine-grained sparse models.
HUME: Measuring the Human-Model Performance Gap in Text Embedding Tasks
Authors: Adnan El Assadi; Isaac Chung; Roman Solomatin; Niklas Muennighoff; Kenneth Enevoldsen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Comparing human and model performance offers a valuable perspective for understanding the strengths and limitations of embedding models, highlighting where they succeed and where they fail to capture meaning and nuance. However, such comparisons are rarely made, as human performance on embedding tasks is difficult to measure. To fill this gap, we introduce HUME: Human Evaluation Framework for Text Embeddings. While frameworks like MTEB provide broad model evaluation, they lack reliable estimates of human performance, limiting the interpretability of model scores. We measure human performance across 16 MTEB datasets spanning reranking, classification, clustering, and semantic textual similarity across linguistically diverse high- and low-resource languages. Humans achieve an average performance of 77.6% compared to 80.1% for the best embedding model, though with substantial variation: models reach high performance on some datasets while struggling on notably low-resource languages. Our human annotations also reveal multiple dataset issues. We additionally benchmark nine LLMs as annotators on reranking, classification, and STS tasks, finding that they fall short of human performance (76.1% vs. 81.2%) despite offering scalability advantages. We provide human performance baselines, insights into task difficulty patterns, and an extensible evaluation framework that enables a more meaningful interpretation of results and informs the development of both models and benchmarks. Our code, dataset, and leaderboard are publicly available at https://github.com/embeddings-benchmark/mteb.
Self-Improving Skill Learning for Robust Skill-based Meta-Reinforcement Learning
Authors: Seungyul Han; Sanghyeon Lee; Sangjun Bae; Yisak Park
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Meta-reinforcement learning (Meta-RL) facilitates rapid adaptation to unseen tasks but faces challenges in long-horizon environments. Skill-based approaches tackle this by decomposing state-action sequences into reusable skills and employing hierarchical decision-making. However, these methods are highly susceptible to noisy offline demonstrations, leading to unstable skill learning and degraded performance. To address this, we propose Self-Improving Skill Learning (SISL), which performs self-guided skill refinement using decoupled high-level and skill improvement policies, while applying skill prioritization via maximum return relabeling to focus updates on task-relevant trajectories, resulting in robust and stable adaptation even under noisy and suboptimal data. By mitigating the effect of noise, SISL achieves reliable skill learning and consistently outperforms other skill-based meta-RL methods on diverse long-horizon tasks. Our code is available at https://github.com/epsilog/SISL.
Mixing Mechanisms: How Language Models Retrieve Bound Entities In-Context
Authors: Yoav Gur-Arieh; Mor Geva; Atticus Geiger
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: A key component of in-context reasoning is the ability of language models (LMs) to bind entities for later retrieval.
For example, an LM might represent *Ann loves pie* by binding *Ann* to *pie*, allowing it to later retrieve *Ann* when asked *Who loves pie?* 
Prior research on short lists of bound entities found strong evidence that LMs implement such retrieval 
via a **positional mechanism**, where *Ann* is retrieved based on its position in context.
In this work, we find that this mechanism generalizes poorly to more complex settings; as the number of bound entities in context increases, the positional mechanism becomes noisy and unreliable in middle positions.
To compensate for this, we find that LMs supplement the positional mechanism with a **lexical mechanism** (retrieving *Ann* using its bound counterpart *pie*) and a **reflexive mechanism** (retrieving *Ann* through a direct pointer). 
Through extensive experiments on nine models and ten binding tasks, we uncover a consistent pattern in how LMs mix these mechanisms to drive model behavior.
We leverage these insights to develop a causal model combining all three mechanisms that estimates next token distributions with 95\% agreement.
Finally, we show that our model generalizes to substantially longer inputs of open-ended text interleaved with entity groups, further demonstrating the robustness of our findings in more natural settings.
Overall, our study establishes a more complete picture of how LMs bind and retrieve entities in-context.
Chasing the Tail: Effective Rubric-based Reward Modeling for Large Language Model Post-Training
Authors: Junkai Zhang; Zihao Wang; Lin Gui; Swarnashree Mysore Sathyendra; Jaehwan Jeong; Victor Veitch; Wei Wang; Yunzhong He; Bing Liu; Lifeng Jin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement fine-tuning (RFT) often suffers from reward over-optimization, where a policy model hacks the reward signals to achieve high scores while producing low-quality outputs. Our theoretical analysis shows that the key lies in reward misspecification at the high-reward tail: the inability to reliably distinguish excellent responses from merely great ones. This motivate us to focus on the high-reward region. However, such tail examples are scarce under the base LLM. While off-policy exemplars (e.g. from stronger models or rewrites) are easier to obtain, naively training on them yields a misspecified reward for the policy we aim to align. To address this, we study rubric-based rewards. By design, rubrics can leverage off-policy examples while remaining insensitive to their artifacts. To elicit rubrics that capture the high-reward tail, we highlight the importance of distinguishing among great and diverse responses, and introduce a workflow to implement this idea. We empirically demonstrate that rubric-based rewards substantially mitigate reward over-optimization and deliver effective LLM post-training improvements.
Measuring the Intrinsic Dimension of Earth Representations
Authors: Arjun Rao; Marc Rußwurm; Konstantin Klemmer; Esther Rolf
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Within the context of representation learning for Earth observation, geographic Implicit Neural Representations (INRs) embed low-dimensional location inputs (longitude, latitude) into high-dimensional embeddings, through models trained on geo-referenced satellite, image or text data. Despite the common aim of geographic INRs to distill Earth's data into compact, learning-friendly representations, we lack an understanding of how much information is contained in these Earth representations, and where that information is concentrated. The intrinsic dimension of a dataset measures the number of degrees of freedom required to capture its local variability, regardless of the ambient high-dimensional space in which it is embedded. This work provides the first study of the intrinsic dimensionality of geographic INRs. Analyzing INRs with ambient dimension between 256 and 512, we find that their intrinsic dimensions fall roughly between 2 and 10 and are sensitive to changing spatial resolution and input modalities during INR pre-training. Furthermore, we show that the intrinsic dimension of a geographic INR correlates with downstream task performance and can capture spatial artifacts, facilitating model evaluation and diagnostics. More broadly, our work offers an architecture-agnostic, label-free metric of information content that can enable unsupervised evaluation, model selection, and pre-training design across INRs.
Convergence of Regret Matching in Potential Games and Constrained Optimization
Authors: Ioannis Anagnostides; Emanuel Tewolde; Brian Hu Zhang; Ioannis Panageas; Vincent Conitzer; Tuomas Sandholm
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Regret matching (RM)---and its modern variants---is a foundational online algorithm that has been at the heart of many AI breakthrough results in solving benchmark zero-sum games, such as poker. Yet, surprisingly little is known so far in theory about its convergence beyond two-player zero-sum games. For example, whether regret matching converges to Nash equilibria in potential games has been an open problem for two decades. Even beyond games, one could try to use RM variants for general constrained optimization problems. Recent empirical evidence suggests that they---particularly regret matching$^+$ (RM$^+$)---attain strong performance on benchmark constrained optimization problems, outperforming traditional gradient descent-type algorithms.

We show that RM$^+$ converges to an $\epsilon$-KKT point after $O_\epsilon(1/\epsilon^4)$ iterations, establishing for the first time that it is a sound and fast first-order optimizer. Our argument relates the KKT gap to the accumulated \emph{regret}, two quantities that are entirely disparate in general but interact in an intriguing way in our setting, so much so that when regrets are bounded, our complexity bound improves all the way to $O_\epsilon(1/\epsilon^2)$. From a technical standpoint, while RM$^+$ does not have the usual one-step improvement property in general, we show that it does in a certain region that the algorithm will quickly reach and remain in thereafter. In contrast, our second main result establishes that RM, with or without alternation, can take an exponential number of iterations to reach a crude approximate solution even in two-player potential games. This represents the first worst-case separation between RM and RM$^+$. Our lower bound shows that convergence to coarse correlated equilibria in potential games is exponentially faster than convergence to Nash equilibria.
Modeling Others' Minds as Code
Authors: Kunal Jha; Aydan Yuenan Huang; Eric Ye; Natasha Jaques; Max Kleiman-Weiner
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Accurate prediction of human behavior is essential for robust and safe human-AI collaboration. However, existing approaches for modeling people are often data-hungry and brittle because they either make unrealistic assumptions about rationality or are too computationally demanding to adapt rapidly. Our key insight is that many everyday social interactions may follow predictable patterns; efficient "scripts" that minimize cognitive load for actors and observers, e.g., "wait for the green light, then go." We propose modeling these routines as behavioral programs instantiated in computer code rather than policies conditioned on beliefs and desires. We introduce ROTE, a novel algorithm that leverages both large language models (LLMs) for synthesizing a hypothesis space of behavioral programs, and probabilistic inference for reasoning about uncertainty over that space. We test ROTE in a suite of gridworld tasks and a large-scale embodied household simulator. ROTE predicts human and AI behaviors from sparse observations, outperforming competitive baselines---including behavior cloning and LLM-based methods---by as much as 50% in terms of in-sample accuracy and out-of-sample generalization. By treating action understanding as a program synthesis problem, ROTE opens a path for AI systems to efficiently and effectively predict human behavior in the real-world.
Splat Regression Models
Authors: Mara Daniels; Philippe Rigollet
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce a highly expressive class of function approximators called *Splat Regression Models*. Model outputs are mixtures of heterogeneous and anisotropic bump functions, termed *splats*, each weighted by an output vector. The power of splat modeling lies in its ability to locally adjust the scale and direction of each splat, achieving both high interpretability and accuracy. Fitting splat models reduces to optimization over the space of mixing measures, which can be implemented using Wasserstein-Fisher-Rao gradient flows. As a byproduct, we recover the popular *Gaussian Splatting* methodology as a special case, providing a unified theoretical framework for this state-of-the-art technique that clearly disambiguates the inverse problem, the model, and the optimization algorithm. Through numerical experiments, we demonstrate that the resulting models and algorithms constitute a flexible and promising approach for solving diverse approximation, estimation, and inverse problems involving low-dimensional data.
CMT-Benchmark: A Benchmark for Condensed Matter Theory Built by Expert Researchers
Authors: Haining Pan; James V Roggeveen; Erez Berg; Juan Felipe Carrasquilla Alvarez; Debanjan Chowdhury; Surya Ganguli; Federico Ghimenti; Juraj Hasik; Henry S. Hunt; Hong-Chen Jiang; Mason Kamb; Ying-Jer Kao; Ehsan Khatami; Michael J Lawler; Di Luo; Titus Neupert; Xiaoliang Qi; Michael Brenner; Eun-Ah Kim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) have demonstrated remarkable progress in coding and mathematical problem-solving; however, evaluation on advanced research-level problems in the hard sciences remains scarce. 
To fill this gap, we present \cmt, a dataset of 50 original problems covering condensed matter theory (CMT) at the level of an expert researcher. The solution for these problems involve analytical and computational approaches commonly used in quantum many-body physics and classical statistical mechanics. The dataset has been designed and verified by a worldwide panel of expert researchers through a collaborative environment. 
Topics in the dataset include Hartree-Fock mean-field theory, exact diagonalization methods, quantum Monte Carlo sampling, density matrix renormalization group, quantum statistical mechanics, classical statistical mechanics, and model building. We evaluate different LLMs by programmatically checking LLM-generated solutions against expert-supplied ground truth. 
To verify LLMs performance at scale, we developed an automated machine-grading pipeline suitable for advanced physics research problems. 
For example, we handle non-commuting operators that are essential for quantum many-body problems by symbolic manipulation and normal ordering. 
Our evaluations show that frontier models struggle with all of the problems in the dataset, highlighting a gap in the physical reasoning skills of current LLMs. Notably, experts identified strategies for creating increasingly difficult problems by interacting with the LLMs and exploiting common failure modes. 
While the highest-performing model, GPT5, correctly solves 30\% of the problems, average performance across 17 models (GPT, Gemini, Claude, DeepSeek, and Llama classes) is only 11.4$\pm$2.1\%.  Moreover, our benchmark contains 18 problems that not a single one of the 17 models considered here can correctly solve, and 26 problems that are solved by at most one model. 
These currently unsolvable problems span the fields of Quantum Monte Carlo, Variational Monte Carlo, and Density Matrix Renormalization Group. 
Furthermore, we illustrate how incorrect answers sometimes violate fundamental symmetries or have unphysical scaling dimensions. We believe that this benchmark set provides valuable guidance for the future development of language models, aiming to achieve the goal of AI research assistants and tutors.
Dyna-Mind: Learning to Simulate from Experience for Better AI Agents
Authors: Xiao Yu; Baolin Peng; Michel Galley; Hao Cheng; Qianhui Wu; Janardhan Kulkarni; Suman Nath; Zhou Yu; Jianfeng Gao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reasoning models have recently shown remarkable progress in domains such as math and coding. However, their expert-level abilities in math and coding contrast sharply with their performance in long-horizon, interactive tasks such as web navigation and computer/phone-use. Inspired by literature on human cognition, we argue that current AI agents need ``vicarious trial and error'' - the capacity to mentally simulate alternative futures before acting - in order to enhance their understanding and performance in complex interactive environments. We introduce Dyna-Mind, a two-stage training framework that explicitly teaches (V)LM agents to integrate such simulation into their reasoning. In stage 1, we introduce Reasoning with Simulations (ReSim), which trains the agent to generate structured reasoning traces from expanded search trees built from real experience gathered through environment interactions. ReSim thus grounds the agent's reasoning in faithful world dynamics and equips it with the ability to anticipate future states in its reasoning. In stage 2, we propose Dyna-GRPO, an online reinforcement learning method to further strengthen the agent's simulation and decision-making ability by using both outcome rewards and intermediate states as feedback from real rollouts. Experiments on two synthetic benchmarks (Sokoban and ALFWorld) and one realistic benchmark (AndroidWorld) demonstrate that (1) ReSim effectively infuses simulation ability into AI agents, and (2) Dyna-GRPO leverages outcome and interaction-level signals to learn better policies for long-horizon, planning-intensive tasks. Together, these results highlight the central role of simulation in enabling AI agents to reason, plan, and act more effectively in the ever more challenging environments.
Multiple Token Divergence: Measuring and Steering In-Context Computation Density
Authors: Vincent Herrmann; Eric Alcaide; Michael Wand; Jürgen Schmidhuber
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Measuring the in-context computational effort of language models is a key challenge, as metrics like next-token loss fail to capture reasoning complexity. Prior methods based on latent state compressibility can be invasive and unstable. We propose Multiple Token Divergence (MTD), a simple measure of computational effort defined as the KL divergence between a model's full output distribution and that of a shallow, auxiliary prediction head. MTD can be computed directly from pre-trained models with multiple prediction heads, requiring no additional training. Building on this, we introduce Divergence Steering, a novel decoding method to control the computational character of generated text. We empirically show that MTD is more effective than prior methods at distinguishing complex tasks from simple ones. On mathematical reasoning benchmarks, MTD correlates positively with problem difficulty. Lower MTD is associated with more accurate reasoning. MTD provides a practical, lightweight tool for analyzing and steering the computational dynamics of language models.