GenAI4ELab/papercli-papers huggingface dataset:
Constrained Decoding of Diffusion LLMs with Context-Free Grammars
Authors: Niels Mündler; Jasper Dekoninck; Martin Vechev
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) have shown promising performance across diverse domains. Many practical applications of LLMs, such as code completion and structured data extraction, require adherence to syntactic constraints specified by a formal language. Yet, due to their probabilistic nature, LLM output is not guaranteed to adhere to such formal languages. To address this, prior work has proposed constrained decoding to restrict LLM generation to particular formal languages. However, existing works are not applicable to the emerging paradigm of diffusion LLMs, as this requires supporting token generation in arbitrary order instead of the traditional left-to-right order. In this paper, we address this challenge and present the first constrained decoding method for diffusion models, one that can handle formal languages captured by context-free grammars. We begin by reducing constrained decoding to the more general additive infilling problem, which asks whether a partial output with holes can be completed to a valid word in the target language. This problem also naturally subsumes the previously unaddressed multi-region infilling constrained decoding. We then reduce this problem to the task of deciding whether the intersection of the target language and a regular language is empty and present an efficient algorithm to solve this task for context-free languages. Empirical results on various applications, such as C++ code infilling and structured data extraction in JSON, demonstrate that our method achieves near-perfect syntactic correctness while consistently preserving or improving functional correctness. Importantly, our efficiency optimizations ensure that the computational overhead remains practical.
Formalising Human-in-the-Loop: Computational Reductions, Failure Modes, and Legal-Moral Responsibility
Authors: Maurice Chiodo; Dennis Müller; Paul Siewert; Jean-Luc Wetherall; Zoya Yasmine; John Burden
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We  use the notion of oracle machines and reductions from computability theory to formalise different Human-in-the-loop (HITL) setups for AI systems, distinguishing between trivial human monitoring (i.e., total functions), single endpoint human action (i.e., many-one reductions), and highly involved human-AI interaction (i.e., Turing reductions). We then proceed to show that the legal status and safety of different setups vary greatly. We present a taxonomy to categorise HITL failure modes, highlighting the practical limitations of  HITL setups. We then identify omissions in UK and EU legal frameworks, which focus on HITL setups that may not always achieve the desired ethical, legal, and sociotechnical outcomes. We suggest areas where the law should recognise the effectiveness of different HITL setups and assign responsibility in these contexts, avoiding human `scapegoating'. Our work shows an unavoidable trade-off between attribution of legal responsibility, and  technical explainability.  Overall, we show how HITL setups involve many technical design decisions, and can be prone to failures out of the humans' control. Our formalisation and taxonomy opens up a new analytic perspective on the challenges in creating HITL setups, helping inform AI developers and lawmakers on designing HITL setups to better achieve their desired outcomes.
Let's (not) just put things in Context: Test-time Training for Long-context LLMs
Authors: Rachit Bansal; Aston Zhang; Rishabh Tiwari; Lovish Madaan; Sai Surya Duvvuri; Fnu Devvrit; David Brandfonbrener; David Alvarez-Melis; Prajjwal Bhargava; Mihir Kale; Samy Jelassi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Progress on training and architecture strategies has enabled LLMs with millions of tokens in context length. However, empirical evidence suggests that such long-context LLMs can consume far more text than they can reliably use. On the other hand, it has been shown that inference-time compute can be used to scale performance of LLMs, often by generating thinking tokens, on challenging tasks involving multi-step reasoning. Through controlled experiments on sandbox long-context tasks, we find that such inference-time strategies show rapidly diminishing returns and fail at long context. We attribute these failures to score dilution, a phenomenon inherent to static self-attention. Further, we show that current inference-time strategies cannot retrieve relevant long-context signals under certain conditions. We propose a simple method that, through targeted gradient updates on the given context, provably overcomes limitations of static self-attention. We find that this shift in how inference-time compute is spent leads to consistently large performance improvements across models and long-context benchmarks. Our method leads to large 12.6 and 14.1 percentage point improvements for Qwen3-4B on average across subsets of LongBench-v2 and ZeroScrolls benchmarks. The takeaway is practical: for long context, a small amount of context-specific training is a better use of inference compute than current inference-time scaling strategies like producing more thinking tokens.
FROST: Filtering Reasoning Outliers with Attention for Efficient Reasoning
Authors: Haozheng Luo; Zhuolin Jiang; Md Zahid Hasan; Yan Chen; Soumalya Sarkar
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose **FROST**, an attention-aware method for efficient reasoning.  Unlike traditional approaches, FROST leverages attention weights to prune uncritical reasoning paths, yielding shorter and more reliable reasoning trajectories.  Methodologically, we introduce the concept of *reasoning outliers* and design an attention-based mechanism to remove them.  Theoretically, FROST preserves and enhances the model’s reasoning capacity while eliminating outliers at the sentence level.  Empirically, we validate FROST on four benchmarks using two strong reasoning models (Phi-4-Reasoning and GPT-oss-20B), outperforming state-of-the-art methods such as TALE and ThinkLess.  Notably, FROST achieves an average **69.68%** reduction in token usage and a **26.70%** improvement in accuracy over the base model.  Furthermore, in evaluations of attention outlier metrics, FROST reduces the maximum infinity norm $\||\mathbf{x}\||_{\infty}$ by **15.97%** and the average kurtosis by **91.09%** compared to the base model.
Guided Speculative Inference for Efficient Test-Time Alignment of LLMs
Authors: Jonathan Geuter; Youssef Mroueh; David Alvarez-Melis
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose Guided Speculative Inference (GSI), a novel algorithm for efficient reward-guided decoding in large language models.
GSI combines soft best-of-$n$ test-time scaling with a reward model $r(x,y)$ and speculative samples from a small auxiliary model $\pi_S(y\mid x)$. We provably approximate both the optimal tilted policy
$\pi_{\beta,B}(y\mid x) \propto \pi_B(y\mid x)\exp(\beta\,r(x,y))$ of soft best-of-$n$ under the base model $\pi_B$, as well as the expected reward under the optimal policy. In experiments on reasoning benchmarks (MATH500, OlympiadBench, Minerva Math, MMLU-STEM, GSM8K) and across different model families, our method achieves higher accuracy than standard soft best-of-$n$ with $\pi_S$ and reward-guided speculative decoding (Liao et al., 2025), and in certain settings even outperforms soft best-of-$n$ with $\pi_B$, while reducing end-to-end latency by up to 28%.
The Illusion of Diminishing Returns: Measuring Long Horizon Execution in LLMs
Authors: Akshit Sinha; Arvindh Arun; Shashwat Goel; Steffen Staab; Jonas Geiping
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Does continued scaling of large language models (LLMs) yield diminishing returns? In this work, we show that short-task benchmarks may give an illusion of slowing progress, as even marginal gains in single-step accuracy can compound into exponential improvements in the length of tasks a model can successfully complete. Then, we argue that failures of LLMs when simple tasks are made longer arise from mistakes in execution, rather than an inability to reason. So, we propose isolating execution capability, by explicitly providing the knowledge and plan needed to solve a long-horizon task. First, we find that larger models can correctly execute significantly more turns even when small models have near-perfect single-turn accuracy. We then observe that the per-step accuracy of models degrades as the number of steps increases. This is not just due to long-context limitations---curiously, we observe a self-conditioning effect---models become more likely to make mistakes when the context contains their errors from prior turns. Self-conditioning does not reduce by just scaling the model size. But, we find that thinking mitigates self-conditioning, and also enables execution of much longer tasks in a single turn. We conclude by benchmarking frontier thinking models on the length of tasks they can execute in a single turn. Overall, by focusing on the ability to execute, we hope to reconcile debates on how LLMs can solve complex reasoning problems yet fail at simple tasks when made longer, and highlight the massive benefits of scaling model size and sequential test-time compute for long-horizon tasks.
Learning a distance measure from the information-estimation geometry of data
Authors: Guy Ohayon; Pierre-Etienne H Fiquet; Florentin Guth; Jona Ballé; Eero P Simoncelli
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce the Information-Estimation Metric (IEM), a novel form of distance function derived from an underlying continuous probability density over a domain of signals.
The IEM is rooted in a fundamental relationship between information theory and estimation theory, which links the log-probability of a signal with the errors of an optimal denoiser, applied to noisy observations of the signal.
In particular, the IEM between a pair of signals is obtained by comparing their denoising error vectors over a range of noise amplitudes.
Geometrically, this amounts to comparing the score vector fields of the *blurred* density around the signals over a range of blur levels.
We prove that the IEM is a valid global distance metric and derive a closed-form expression for its local second-order approximation, which yields a Riemannian metric.
For Gaussian-distributed signals, the IEM coincides with the Mahalanobis distance.
But for more complex distributions, it adapts, both locally and globally, to the geometry of the distribution.
In practice, the IEM can be computed using a learned denoiser (analogous to generative diffusion models) and solving a one-dimensional integral.
To demonstrate the value of our framework, we learn an IEM on the ImageNet database.
Experiments show that this IEM is competitive with or outperforms state-of-the-art supervised image quality metrics in predicting human perceptual judgments.
VL-JEPA: Joint Embedding Predictive Architecture for Vision-language
Authors: Delong Chen; Mustafa Shukor; Théo Moutakanni; Willy Chung; Lei Yu; Tejaswi Kasarla; Allen Bolourchi; Yann LeCun; Pascale Fung
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce VL-JEPA, a vision-language model built on a Joint Embedding Predictive Architecture (JEPA). Instead of autoregressively generating tokens as in classical VLMs, VL-JEPA predicts continuous embeddings of the target texts. By learning in an abstract representation space, the model focuses on task-relevant semantics while abstracting away surface-level linguistic variability. In a strictly controlled comparison against standard token-space VLM training with the same vision encoder and training data, VL-JEPA achieves stronger performance while having 50% fewer trainable parameters. At inference time, a lightweight text decoder is invoked only when needed to translate VL-JEPA predicted embeddings into text. We show that VL-JEPA natively supports selective decoding that reduces the number of decoding operations by ~2.85× while maintaining similar performance compared to non-adaptive uniform decoding. Beyond generation, VL-JEPA's embedding space naturally supports open-vocabulary classification, text-to-video retrieval, and discriminative VQA without any architecture modification. On eight video classification and eight video retrieval datasets, the average performance of VL-JEPA surpasses that of CLIP, SigLIP2, and Perception Encoder. At the same time, the model achieves comparable performance to classical VLMs (InstructBLIP, QwenVL) on four VQA datasets—GQA, TallyQA, POPE, and POPEv2—despite having only 1.6B parameters.
Verification of the Implicit World Model in a Generative Model via Adversarial Sequences
Authors: András Balogh; Márk Jelasity
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Generative sequence models are typically trained on sample sequences from natural or formal languages. It is a crucial question whether&mdash;or to what extent&mdash;sample-based training is able to capture the true structure of these languages, often referred to as the "world model". Theoretical results indicate that we can hope for soundness at best, that is, generating valid sequences, but not necessarily all of them. However, it is still important to have practical tools that are able to verify whether a given sequence model is sound. In this study, we focus on chess, as it is a domain that provides enough complexity while having a simple rule-based world model. We propose adversarial sequence generation for verifying the soundness of the sequence model. Our adversaries generate valid sequences so as to force the sequence model to generate an invalid next move prediction. Apart from the falsification of soundness, this method is also suitable for a more fine-grained analysis of the failure modes and the effects of different choices during training. To demonstrate this, we propose a number of methods for adversarial sequence generation and evaluate the approach on a large set of chess models. We train models on random as well as high-quality chess games, using several training recipes. We find that none of the models are sound, but some training techniques and dataset choices are able to improve soundness remarkably. We also investigate the potential application of board state probes in both our training and attack methods. Our findings indicate that the extracted board states have no causal role in next token prediction in most of the models.
Mixture of Contexts for Long Video Generation
Authors: Shengqu Cai; Ceyuan Yang; Lvmin Zhang; Yuwei Guo; Junfei Xiao; Ziyan Yang; Yinghao Xu; Zhenheng Yang; Alan Yuille; Leonidas Guibas; Maneesh Agrawala; Lu Jiang; Gordon Wetzstein
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Long-context video generation is fundamentally a memory problem: models must retain and retrieve salient events across long range without collapsing or drifting. However, scaling diffusion transformers (DiTs) to generate long-context videos is fundamentally limited by the quadratic cost of self-attention, which makes memory and computation intractable and difficult to optimize for long sequences. We recast long-context video generation as an internal information retrieval task and propose a simple, learnable sparse attention routing module, Mixture of Contexts (MoC), as an effective long-term memory retrieval engine. In MoC, each query dynamically selects a few informative chunks plus mandatory anchors (caption, local windows) to attend to, with causal routing that prevents loop closures. As we scale the data and gradually sparsify the routing, the model allocates compute to salient history, preserving identities, actions, and scenes over minutes of content.
Efficiency follows as a byproduct of retrieval (near-linear scaling), which enables practical training and synthesis, and the emergence of memory and consistency at the scale of minutes. Project Page: https://primecai.github.io/moc/.
Human Uncertainty-Aware Data Selection and Automatic Labeling in Visual Question Answering
Authors: Jian Lan; Zhicheng Liu; Udo Schlegel; Raoyuan Zhao; Yihong Liu; Hinrich Schuetze; Michael A. Hedderich; Thomas Seidl
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large vision-language models (VLMs) achieve strong performance in Visual Question Answering but still rely heavily on supervised fine-tuning (SFT) with massive labeled datasets, which is costly due to human annotations.  Crucially, real-world datasets often exhibit *human uncertainty* (**HU**) — variation in human confidence across annotations, but standard SFT simply optimizes toward the most frequent label, disregarding HU distributions.  This leaves two open questions: *How does HU affect SFT*, and *how can HU be effectively leveraged in training?*  In this work, we first conduct a systematic evaluation of VLMs across varying HU levels. We have two key findings:  (i) surprisingly, high-HU samples contribute little, or even degrade, model performance, and  (ii) naively training on the full dataset yields under-calibrated models that fail to capture HU distributions.  Motivated by these findings, we introduce **HaDola**, a **h**uman uncertainty-**a**ware **d**ata selection and aut**o**matic **la**beling framework.  HaDola operates in four stages: **discriminate**, **self-annotate**, **error trigger**, and **training**, to iteratively identify harmful samples, prioritize informative ones, and bootstrap from a small seed set (5% of data).  Our approach substantially reduces reliance on costly HU annotations and makes VLMs more accurate and better calibrated.  Extensive experiments on VQAv2 and VizWiz datasets demonstrate that HaDola consistently matches or outperforms state-of-the-art baselines, with less training data.  Our work highlights the importance of explicitly modeling HU in SFT, suggesting better utilization of HU is more effective than merely scaling up dataset size.
Eliciting Harmful Capabilities by Fine-Tuning on Safeguarded Outputs
Authors: Jackson Kaunismaa; John Hughes; Christina Q Knight; Avery Griffin; Mrinank Sharma; Erik Jones
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Model developers implement safeguards in frontier models to prevent misuse, for example, by employing classifiers to filter dangerous outputs. In this work, we demonstrate that even robustly safeguarded models can be used to elicit harmful capabilities in open-source models through \textit{elicitation attacks}. Our elicitation attacks consist of three stages: 
(i) constructing prompts in adjacent domains to a target harmful task that do not request dangerous information; (ii) obtaining responses to these prompts from safeguarded frontier models;
(iii) fine-tuning open-source models on these prompt-output pairs. Since the requested prompts cannot be used to directly cause harm, they are not refused by frontier model safeguards. We evaluate these elicitation attacks within the domain of hazardous chemical synthesis and processing, and demonstrate that our attacks recover approximately 40\% of the capability gap between the base open-source model and an unrestricted frontier model. We then show that the efficacy of elicitation attacks scales with the capability of the frontier model and the amount of generated fine-tuning data. Our work demonstrates the challenge of mitigating ecosystem level risks with output-level safeguards.
Beyond Linear Probes: Dynamic Safety Monitoring for Language Models
Authors: James Oldfield; Philip Torr; Ioannis Patras; Adel Bibi; Fazl Barez
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Monitoring large language models' (LLMs) activations is an effective way to detect harmful requests before they lead to unsafe outputs. However, traditional safety monitors often require the same amount of compute for every query. This creates a trade-off: expensive monitors waste resources on easy inputs, while cheap ones risk missing subtle cases. We argue that safety monitors should be flexible--costs should rise only when inputs are difficult to assess, or when more compute is available. To achieve this, we introduce Truncated Polynomial Classifiers (TPCs), a natural extension of linear probes for dynamic activation monitoring. Our key insight is that polynomials can be trained and evaluated progressively, term-by-term. At test-time, one can early-stop for lightweight monitoring, or use more terms for stronger guardrails when needed. TPCs provide two modes of use. First, as a safety dial: by evaluating more terms, developers and regulators can "buy" stronger guardrails from the same model. Second, as an adaptive cascade: clear cases exit early after low-order checks, and higher-order guardrails are evaluated only for ambiguous inputs, reducing overall monitoring costs. On WildGuardMix, across 4 models with up to 30B parameters, we show that TPCs compete with or outperform MLP-based probe baselines of the same size for harmful prompt classification, all the while being more interpretable than their black-box counterparts.
One-Prompt Strikes Back: Sparse Mixture of Experts for Prompt-based Continual Learning
Authors: Minh Le; Bao-Ngoc Dao; Huy Nguyen; Quyen Tran; Anh Nguyen; Nhat Ho
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Prompt-based methods have recently gained prominence in Continual Learning (CL) due to their strong performance and memory efficiency. A prevalent strategy in this paradigm assigns a dedicated subset of prompts to each task, which, while effective, incurs substantial computational overhead and causes memory requirements to scale linearly with the number of tasks. Conversely, approaches employing a single shared prompt across tasks offer greater efficiency but often suffer from degraded performance due to knowledge interference. To reconcile this trade-off, we propose **SMoPE**, a novel framework that integrates the benefits of both task-specific and shared prompt strategies. Inspired by recent findings on the relationship between Prefix Tuning and Mixture of Experts (MoE), SMoPE organizes a shared prompt into multiple "prompt experts" within a sparse MoE architecture. For each input, only a select subset of relevant experts is activated, effectively mitigating interference. To facilitate expert selection, we introduce a prompt-attention score aggregation mechanism that computes a unified proxy score for each expert, enabling dynamic and sparse activation. Additionally, we propose an adaptive noise mechanism to encourage balanced expert utilization while preserving knowledge from prior tasks. To further enhance expert specialization, we design a prototype-based loss function that leverages prefix keys as implicit memory representations. Extensive experiments across multiple CL benchmarks demonstrate that SMoPE consistently outperforms task-specific prompt methods and achieves performance competitive with state-of-the-art approaches, all while significantly reducing parameter counts and computational costs.