NeurIPS 2026 论文列表

Causal modeling of physical temporal phenomena must handle interventions that act along trajectories, nonstationary induced laws, path-dependent effects, and feedback mediated by dynamics, all challenging in standard causal models. We introduce \emph{Hamiltonian Causal Models} (HCMs), a trajectory-level framework in which observed variables interact with local environments and interventions act as controls of Hamiltonian mechanisms. HCMs separate immutable equations of motion from intervenable mechanisms and define causal effects as discrepancies between interventional path laws. A key motivation for HCMs is their natural interface with non-equilibrium thermodynamics. Entropy production quantifies the irreversibility of a process and is a central causal observable: it is estimable from data and witnesses causal effects along the system's evolution that are invisible to endpoint and cumulative versions of the standard average treatment effect. As in physics, cause-and effect are not primitives of the relation between two random variables but arise from the non-invertibility of the thermodynamic arrow. With this, our paper reconciles the language of statistical causal models and non-stationary thermodynamics, offering new tools to describe causality in a wide range of physical systems.

User modeling aims to use language models (LMs) to mimic an individual’s behavior from a corpus of past context–action pairs (e.g., conversation turns), enabling the simulation of users in settings like behavioral science, human–AI collaboration, and market research. Recent approaches augment these corpora with synthesized reasoning traces, typically generated by conditioning on both context and action. However, such conditioning constitutes post-hoc rationalization rather than reasoning: the trace is guaranteed to justify the action, but may not encode the underlying latent causal decision paths. We propose Recon, which uses action reconstruction to score reasoning traces by their predictive power: given a context and candidate reasoning, a reconstruction model predicts the action, and reconstruction fidelity determines reasoning quality. Across four domains, Recon achieves a 54.7\% win rate over Backward Synthesis, a standard post-hoc rationalization baseline. Further, we find that training a reasoning synthesis model with rewards derived from Recon improves downstream user modeling performance, achieving a win rate of up to 70.0\% over baselines. We further show that Recon-synthesized reasoning transfers across models, and improves user modeling beyond the reconstruction model. Our work demonstrates that post-hoc rationalization is insufficient for reasoning synthesis, and that useful and interpretable reasoning should naturally elicit the action from the context.

Sequential recommenders need long histories, but full-prefix attention scales with user lifetime. We argue that an effective memory mechanism should provide space invariance, semantic fidelity, and lifelong evolvability, which existing fixed-window or capped-memory methods do not jointly satisfy. We propose RecMem, a recurrent compression framework that folds history segments into M forget-gated memory slots. RecMem has a history-length-independent rollout error bound, and its fixed-capacity memory is trained by next-item prediction to preserve useful signals. On MerRec, RecMem uses 5.6x fewer decoder-side tokens than full attention while retaining 88–97% of Recall@10–200. Compared with a compressed truncation baseline at similar decoder cost, RecMem improves Recall@50+ by 4–6 percentage points by compressing the full history rather than discarding old interactions. It also remains stable over 1,800 test steps.

Self-distillation has emerged as a powerful framework for post-training LLMs, where a teacher conditioned on extra information guides a student without it, both from the same model. While this guidance is useful when the student has failed, on successful rollouts, the same mechanism instead overwrites the student's choices and suppresses it's own reasoning. Therefore, we propose reading the original self-distillation signal in reverse: when the student succeeds along a path the teacher would not have predicted, these tokens reflect its self-driven reasoning. Building on this, we propose RLRT (RLVR with Reversed Teacher), which augments GRPO by reinforcing these tokens on correct rollouts. We interpret this as a new form of exploration in RLVR: not uniform diversity, but valuable exploration grounded in the student's own success. Across base, instruction-tuned, and thinking-tuned Qwen3 checkpoints, RLRT substantially outperforms self-distillation and exploration-based baselines, establishing information asymmetry as a new, principled design axis for RLVR.

Image-to-video models often generate videos that remain overly static, compared to text-to-video models. While prior approaches mitigate this issue by weakening or modifying the image-conditioning signal, they often require additional training or sacrifice fidelity to the reference image. In this work, we identify reference-frame dominance as a key mechanism behind motion suppression. We observe that non-reference frames in I2V models allocate excessive self-attention to reference-frame key tokens, causing reference information to be over-propagated across time and suppressing inter-frame dynamics. Based on this finding, we propose DyMoS (Dynamic Motion Slider), a training-free and model-agnostic method that rebalances the attention pathway from generated frames to the reference frame during initial denoising steps. DyMoS leaves both the input image and model weights unchanged and introduces a single scalar parameter for continuous control over motion strength. Experiments across multiple state-of-the-art I2V backbones demonstrate that DyMoS consistently improves motion dynamics while maintaining visual quality and fidelity to the reference image.

Large Reasoning Models (LRMs) leverage explicit reasoning traces, known as Chain-of-Thought (CoT) reasoning, to decompose complex problems into intermediate steps before deriving final answers. However, these reasoning traces introduce unique safety challenges: harmful content can be embedded in intermediate steps even when final answers appear benign. Our study reveals that existing state-of-the-art moderation tools experience significant performance degradation on CoT moderation, with F1 scores dropping by up to 35.2% compared to traditional answer moderation. To address these challenges, we present ReasoningShield, a comprehensive benchmark and a suite of strong lightweight models for CoT safety moderation. Our benchmark includes 9.2K annotated Query-CoT pairs with structured stepwise risk analysis, consisting of a 7K-sample training set and a 2.2K human-annotated test set, spanning 10 risk categories, 3 safety levels, and 8 LRMs with diverse reasoning paradigms. To further demonstrate the utility of the benchmark, we develop lightweight models with 1B and 3B parameters using a two-stage training strategy. Our models achieve 91.8% F1 on our benchmark, substantially outperforming leading tools such as LlamaGuard-4 by 35.6% and commercial models such as GPT-4o by 15.8%, while generalizing effectively across diverse reasoning paradigms and unseen scenarios. All resources, including the dataset, code, and models, are released at https://anonymous.4open.science/r/ReasoningShield.

Grounding natural language queries to target objects in images requires both high-level reasoning about intentions and precise spatial localization. Multimodal large language models (MLLMs) excel at reasoning about which object is desired given an implicit intention query but turning this understanding into precise bounding box predictions remains challenging. Conversely, specialized detectors such as GroundingDINO offer robust localization capabilities but lack the inherent capacity to understand implicit intentions. Existing hybrid methods couple these components only at the semantic level, such as by passing object names or a single global token to the detector, thereby discarding the rich, dense spatial signals encoded in MLLM attention maps. We propose Reasoning-based Spatial Prior (RSP), a reasoning-based detection framework that converts the MLLM’s internal attention into a learnable spatial prior and injects it directly into the detector. Concretely, we learn input-adaptive gating over attention heads, supervise the fused spatial prior with a SoftIoU localization loss, and integrate it into GroundingDINO via a prior-guided deformable fusion module that biases sampling toward regions highlighted by the reasoning process. Our method consistently outperforms strong MLLM-only, detector-only, and hybrid baselines on the EgoIntention and RIO benchmarks, achieving state-of-the-art performance with substantial gains across both frequent and uncommon categories.

Explicit reasoning models are trained to produce intermediate reasoning traces before final answers, but downstream fine-tuning is often performed on ordinary instruction--response data that contains no such traces. We show that this mismatch can induce *reasoning-trace collapse*: a fine-tuned model continues to produce plausible final answers while losing the structurally valid explicit reasoning traces that made it a reasoning model in the first place. We introduce a structural evaluation framework that separates answer correctness from reasoning-trace validity, measuring valid, empty, missing, and truncated reasoning alongside reasoning-conditioned task performance. Using this framework, we study open-weight reasoning models and find that standard supervised fine-tuning can rapidly suppress valid reasoning traces, and that answer-only metrics can substantially obscure this failure: in several settings, performance conditional on valid reasoning remains high while the rate of valid reasoning falls sharply. We further show that simple loss-masking strategies substantially mitigate collapse without requiring teacher-generated reasoning traces. These results suggest that evaluations of fine-tuned reasoning models should report structural reasoning reliability in addition to final-answer performance, especially when adaptation data does not contain explicit reasoning traces.

Reasoning models increasingly depend on test-time computation, but raw accuracy conflates intrinsic ability, problem difficulty, guessing effects, and reasoning budget. Item Response Theory (IRT) adjusts for problem difficulty, yet collapses test-time compute into a single latent score. We propose reasoning-aware IRT, an extension in which each model's effective ability depends on baseline ability, reasoning-budget responsiveness, and overthinking curvature. Parameter estimation is performed via multi-pass generalized EM with post-hoc initialization refinement. On LiveBench with 42 language models, our method improves rank stability, gives stronger held-out correctness discrimination, and reveals interpretable structure unavailable to classical IRT: a unified benchmark--ability scale with quantified reasoning-token benefits, budget-efficient and overthinking regimes, and rank reversals obscured by raw scores. Our framework offers a compact, explainable evaluation tool for reasoning models under test-time scaling.

Discrete diffusion models have recently become competitive with autoregressive models for language modeling, even outperforming them on reasoning tasks requiring planning and global coherence, but diffusion requires more computation at inference time. We trace this trade-off to a key mechanism: diffusion models are trained to jointly predict all unknown tokens simultaneously, including those that will not actually be decoded in the current step. Ablating this joint prediction yields faster inference but degrades performance, revealing that accurate prediction at the decoded position relies on joint reasoning about the undecoded tokens. We interpret these undecoded positions as latent tokens and introduce a method for modulating their number, achieving a smooth tradeoff between inference speed and sample quality. Furthermore, we demonstrate that latent tokens can be introduced into autoregressive models through an auxiliary multi-token prediction objective, yielding substantial improvements on the same reasoning tasks where they have traditionally struggled. Our results suggest that latent tokens--arising from jointly predicting multiple unknown positions--represent a general mechanism for improving performance on tasks requiring global coherence or lookahead.

Frontier reasoning models are produced by post-training base language models with reinforcement learning. Recent work has challenged this by showing that sampling from a sharpened version of the base model's distribution, a so-called *power distribution,* elicits comparable reasoning without additional training, curated datasets, or verifiers. However, making this method practical requires *efficiently sampling* from the power distribution. A sampler needs to "mix" to the power distribution which necessitates moving between modes of the target distribution; intuitively, e.g., trying different reasoning strategies. The samplers proposed in prior works repeatedly select a "cut" position in the current reasoning trace uniformly at random and resamples the suffix from that position onwards. However, reasoning traces typically contain a few consequential decisions (e.g., the choice of proof strategy or algorithm), and we observe that a uniformly chosen cut tends to rewrites local details rather than revisiting decision points. We introduce an algorithm (Entropy-Cut Metropolis--Hastings) which uses the base model's next-token entropy as a proxy to identify key decision points and re-sample from these positions. We empirically verify that entropy jumps are a useful proxy for decision points, and in a stylized model of reasoning prove that our method's mixing time scales with the number of decisions in a trace rather than with the number of tokens, which can be much larger. Across MATH500, HumanEval, GPQA, and AIME on several models, our method consistently improves over baselines and RL-trained models.

Conditional message passing (CMP) currently represents a leading paradigm for knowledge graph reasoning. Since vanilla CMP performs full-graph propagation per query, existing methods adopt subgraph-wise sampling to improve computational efficiency. However, these extracted subgraphs still exhibit high structural overlap. Consequently, the same local structures are repeatedly accessed and processed, leading to substantial redundancy that remains a major bottleneck for scaling reasoning to large knowledge graphs. To address this issue, we propose reasoning over \textbf{Co}upled \textbf{R}eceptive \textbf{F}ields (CoRF) to eliminate subgraph redundancy at scale. Rather than sampling a separate subgraph for each query, CoRF constructs a set of receptive supports that allow multiple queries to share receptive fields, thereby reducing subgraph redundancy. Meanwhile, we introduce boundary padding and residual compensation mechanisms to ensure topological integrity and query coverage. We further incorporate cost-aware load balancing to improve distributed execution efficiency. Comprehensive experiments on state-of-the-art CMP models demonstrate that CoRF achieves up to a 5.9$\times$ speedup and a 10.0$\times$ reduction in memory usage, without compromising reasoning accuracy.

CoT prompting improves LLM accuracy on complex tasks but often increases token usage and inference cost. Existing "Budget Forcing" methods reduce cost via fine-tuning with heuristic length penalties, suppressing both essential reasoning and redundant filler. We recast efficient reasoning as a lossy compression problem under the IB principle, and identify a key theoretical gap when applying naive IB to transformers: attention violates the Markov property between prompt, reasoning trace, and response. To resolve this issue, we model CoT generation under the CIB principle, where the reasoning trace $Z$ acts as a computational bridge that contains only the information about the response $Y$ that is not directly accessible from the prompt $X$. This yields a general Reinforcement Learning objective: maximize task reward while compressing completions under a prior over reasoning traces, subsuming common heuristics (e.g., length penalties) as special cases (e.g., uniform priors). In contrast to naive token-counting approaches, we introduce a semantic prior that measures token cost by surprisal under a language model. Crucially, the prior is queried only for token-level log-probabilities, adding negligible overhead to the training loop. Empirically, our CIB objective prunes reasoning redundancy while preserving fluency and logic, improving accuracy at moderate compression and enabling aggressive compression with minimal accuracy drop. These gains generalize across model families and task domains, confirming CIB as a domain-agnostic CoT compression framework.

Improving the reasoning ability of large language models (LLMs) without ground-truth supervision remains a central challenge in reinforcement learning (RL). Recent label-free RL methods typically rely on internal pseudo-rewards such as self-consistency or majority voting. However, for complex multi-step reasoning, these signals can be systematically misleading: models may repeatedly produce mutually consistent but incorrect trajectories, causing optimization to favor frequency over correctness. In this work, we propose reasoning warm-up reinforcement learning, a label-free RL framework motivated by an empirical phenomenon we call **Reasoning Warm-up**. We observe that when a model successfully completes a deterministic auxiliary task with an exact verifier, its success probability on a subsequent complex reasoning task increases substantially. This coupling suggests that auxiliary-task verification can serve as a useful surrogate signal for selecting and optimizing reasoning trajectories even when the main task itself is not directly verifiable. Based on this observation, we incorporate verifiable auxiliary tasks into both generation and optimization. During generation, the auxiliary task provides verifiable warm-up prefix; during training, its verification outcome is used to score candidate trajectories for policy optimization. Experiments on multiple benchmarks demonstrate the effectiveness of our method.

Long chain-of-thought (CoT) reasoning in large language models often fails in recurring ways: models loop through redundant derivations, drift through uncertain intermediate states, commit prematurely to unsupported answers, or continue generating after a valid conclusion has been reached. These failures are typically treated as separate surface phenomena and addressed with symptom-specific heuristics, which (i) leave the underlying trajectory failure unchanged when they suppress the visible symptom, and (ii) provide no shared substrate for comparing fixes across models or pathologies. We instead study them as failures at the level of CoT reasoning latent state transitions. We introduce a diagnostic framework that abstracts chain-of-thought reasoning as a trajectory through discrete latent reasoning states, yielding a positional taxonomy of six reasoning pathologies, each paired with a transition-grounded detection predicate and a matched inference-time controller. We evaluate the framework through controlled-surgery experiments across multiple datasets and models, spanning arithmetic reasoning, multi-hop question answering, and knowledge-intensive reasoning. Across evaluations, we find that reasoning pathologies exhibit three main diagnostic patterns: \textbf{(1)} failures follow a phase-ordered structure, where termination failures are most reliably identified, early branching failures are detectable, and progression failures require finer graph-aware analyses; \textbf{(2)} counterfactual transition-versus-emission tests show that several detectors track latent trajectory changes rather than surface rewrites, while also exposing cases where paraphrases alter the reasoning path itself; and \textbf{(3)} detector-timed interventions are most effective when the detected pathology has a clear local onset, but do not uniformly dominate generic controls. Together, these findings position the framework as a reproducible diagnostic substrate for studying CoT failures, with explicit evidence boundaries and stronger closed-loop control.

Humans rapidly learn abstract knowledge when encountering novel environments and flexibly deploy this knowledge to guide efficient and intelligent action. Can modern AI systems learn and plan in a similar way? We study this question using a dataset of complex human gameplay with concurrent fMRI recordings, in which participants learn novel video games that require rule discovery, hypothesis revision, and multi-step planning. We jointly evaluate models by their ability to play the games, match human learning behavior, and predict brain activity during the same task, comparing a suite of frontier Large Reasoning Models (LRMs) against model-free and model-based deep reinforcement learning agents and a Bayesian theory-based agent. We find that frontier LRMs most closely match human behavioral patterns during game discovery and predict brain activity an order of magnitude better than both reinforcement learning alternatives across cortical and subcortical regions, with effects robust to permutation controls. Through targeted manipulations, we further show that brain alignment reflects the model's in-context representation of the game state rather than its downstream planning or reasoning. Our results establish LRMs as compelling computational accounts of human learning and decision making in complex, naturalistic environments.

Diffusion-based vision-language-action models (dVLAs) are promising for embodied intelligence but are fundamentally limited in real-time deployment by the high latency of full inference. We propose Realtime-VLA FLASH, a speculative inference framework that eliminates most full inference calls during replanning by introducing a lightweight draft model with parallel verification via the main model's Action Expert and a phase-aware fallback mechanism that reverts to the full inference pipeline when needed. This design enables low-latency, high-frequency replanning without sacrificing reliability. Experiments show that on LIBERO, FLASH largely preserves task performance by replacing many 58.0\,ms full-inference rounds with speculative rounds as fast as 7.8\,ms, lowering task-level average inference latency to 19.1\,ms (3.04$\times$ speedup). We additionally demonstrate effectiveness on real-world conveyor-belt sorting, highlighting its practical impact for latency-critical embodied tasks.

AI systems are increasingly deployed in conversational settings where users may be uncertain whether they are speaking with a human or an AI. Despite mounting regulatory attention to this known safety risk, existing evaluations of AI disclosure are typically English-only, based on machine-generated questions, and restricted to text. We present RealityTest to comprehensively test whether AI systems disclose their identity when asked. The benchmark is the first large-scale multimodal and multilingual evaluation, grounded in human data on how people actually encounter and question AI identity in the real-world. Alongside the benchmark, we release the underlying dataset of 3,152 identity-probing queries collected from $\textasciitilde$750 participants across 49 countries and five languages, in text and speech scenarios. We find that only 31\% of people ask about identity directly, and that the questions people ask are far more diverse than machine-generated queries. We test 17 text and 6 speech models, and find substantial variation in disclosure behaviour. However, a single suppression instruction reduces disclosure rates to below 30\%, even in the best-performing models. Validating our investment in diverse, human-grounded evaluation data, we find that how the question is phrased and the context of the conversation matter more for disclosure than which model is being tested. Safety evaluations built on narrow or synthetic query sets risk mischaracterising how models behave in realistic deployment settings.

Event sequences are chains of random events, each described by heterogeneous (numerical and categorical) features. The problem of event sequence forecasting occurs in finance, healthcare, business process intelligence, and other fields. In this work, we study long-horizon forecasting of event sequences as a conditional generative modeling problem. We evaluate forecast quality from two perspectives: prediction accuracy and distributional realism. To support this, we present a benchmark that contributes metrics for both aspects, strong statistical baselines, recent generative models adapted from adjacent fields, and curated real-world datasets. Careful evaluation reveals that no method excels at both accuracy and realism, highlighting a fundamental trade-off between the two. Finally, we introduce a procedure that aggregates samples from a realism-strong generative model into a state-of-the-art predictor, demonstrating that distributional realism can be converted into prediction accuracy.