NeurIPS 2026 Papers

Agentic large language models are increasingly used to solve real-world tasks by reasoning over goals, invoking tools, and interacting with external environments. Reinforcement learning provides a natural framework for improving these behaviors, and recent agent RL methods have achieved strong results across domains. However, the training dynamics of agent RL remain poorly understood, limiting our ability to diagnose instabilities and design more effective training algorithms. In this work, we identify a previously underexplored phenomenon in agent RL, which we term \emph{cyclical entropy eruption}. Unlike single-turn reasoning RL, where entropy typically collapses and stays low, agent RL training exhibits unique recurring cycles of sharp entropy eruption and gradual subsidence. We decompose this dynamic into three phases and provide theoretical and empirical analyses of each, explaining the mechanisms underlying its cyclical oscillation. We further show that degenerate patterns such as sentence duplication and hallucination, once acquired during eruption, can persist and accumulate across cycles. Motivated by these findings, we propose SEAL (Separation-Enhanced Agent Learning), a lightweight auxiliary loss that separates correct and incorrect trajectories in representation space, directly targeting the root cause of entropy eruption. Experiments across multiple benchmarks, models, and RL algorithms demonstrate that SEAL stabilizes training and yields stronger downstream agent performance.

Large Language Models (LLMs) often generate factually incorrect outputs, commonly termed hallucinations, that undermine trust and limit deployment in high-stakes settings. Existing hallucination detection methods typically require multiple forward passes, or access to model internals. In this work, we provide theoretical background and empirical evidence that the *distribution* of token-level entropies, beyond the mean captured by perplexity or length-normalised entropy, serves as a fingerprint of hallucination, with distributional shape and tail behaviour carrying substantial independent signal. We formalize hallucination detection as a statistical hypothesis test and propose the **C**alibrated **E**ntropy **S**core (**CES**), a lightweight algorithm requiring only a single forward pass and black-box access to token logits. CES combines the mean signal with the maximum signal of the generated entropy through a calibrated reference CDF, producing scores that are directly comparable across models and tasks. We establish finite-sample calibration guarantees via a novel random-length Dvoretzky--Kiefer--Wolfowitz inequality, and also prove that CES detects hallucinations with probability converging to one exponentially fast in the generation length. Across eight QA benchmarks and ten generator models spanning open-source and API access models, CES achieves the highest detection performance among all single-pass black-box methods while providing formal error guarantees that existing heuristics lack. Remarkably, CES is statistically indistinguishable from multi-sample methods that require far greater computational cost, closing the gap between lightweight and expensive detection and making it suitable for real-time, large-scale deployment.

Simulating trajectories of multi-particle systems on complex energy landscapes is a central task in molecular dynamics (MD) and drug discovery, but remains challenging at scale due to computationally expensive and long simulations. Previous approaches leverage techniques such as flow or Schrödinger bridge matching to implicitly learn joint trajectories through data snapshots. However, many systems, including biomolecular systems and heterogeneous cell populations, undergo *dynamic* interactions that evolve over their trajectory and cannot be captured through static snapshots. To close this gap, we introduce **Entangled Schrödinger Bridge Matching (EntangledSBM)**, a framework that learns the first- and second-order stochastic dynamics of interacting, multi-particle systems where the direction and magnitude of each particle's path depend dynamically on the paths of the other particles. We define the Entangled Schrödinger Bridge (EntangledSB) problem as solving a coupled system of bias forces that *entangle* particle velocities. We show that our framework accurately simulates heterogeneous cell populations under perturbations and rare transitions in high-dimensional biomolecular systems. Anonymous code is provided at https://anonymous.4open.science/r/EntangledSBManon.

Combining the predictions of multiple models is one of the most effective strategies in machine learning. Thus, it is only natural to investigate the ensembling of language models (LMs). However, because LMs define distributions over strings, a proper ensemble requires aggregating predictions at the string level, which requires a computation that is generally intractable. In this work, we cast LM ensembling as an inference problem and propose the deployment of sequential Monte Carlo (SMC), an appropriate approximate inference scheme. Concretely, we introduce a unified framework for composing multiple LMs into ensemble distributions parameterized by a broad family of aggregation functions. To sample from these distributions, we introduce an SMC algorithm that operates in a shared character space, enabling ensembles of models with mismatching vocabularies and consistent sampling in the limit. We evaluate a range of ensembles across prompt and model combinations for various text generation tasks, finding that consensus-seeking strategies such as the product consistently outperform probability averaging, and that better posterior approximations can yield better ensemble performance.

Accurate time-series forecasting is critical for a wide range of problems involving temporal data. Ensemble modeling is a well-established technique for leveraging multiple predictive models to increase accuracy and robustness, as the performance of a single predictor can be highly variable due to shifts in the underlying data distribution. This paper proposes a new methodology for building robust ensembles of time series forecasting models. Our approach uses Adaptive Robust Optimization (ARO) to construct a linear regression ensemble whose model weights adapt over time. We demonstrate the effectiveness of our method through a series of synthetic experiments and real-world applications, including air pollution management, energy consumption forecasting, and tropical cyclone intensity forecasting. Our results show that our adaptive ensembles outperform the best ensemble member in hindsight by 16-26\% in root mean square error and 14-28\% in conditional value at risk and improve over competitive ensemble techniques.

We study zeroth-order optimisation under context distributional uncertainty, a setting commonly tackled using Bayesian optimisation (BO). A prevailing strategy to make BO more robust to the complex and noisy nature of data is to employ an ensemble as the surrogate model, thereby mitigating the weaknesses of any single model. In this study, we propose a novel algorithm for Ensemble Distributionally Robust Bayesian Optimisation that remains computationally tractable while managing continuous context. We obtain theoretical sublinear regret bounds, improving current state-of-the-art results. We show that our method’s empirical behaviour aligns with its theoretical guarantees.

Speech large language models (SpeechLMs) can process spoken requests, yet still lag behind text-prompted large language models (LLMs) on instruction following and reasoning. We argue that this gap is not only an ASR or representation problem: the same semantic instruction can induce different response policies when spoken with different speakers, accents, noise conditions, prosody, or disfluencies. We propose Verifiable Invariant Reinforced Behavior Alignment (VIRBA), a reinforcement-learning framework for aligning SpeechLM behavior across acoustic realizations of the same semantic intent. VIRBA builds multi-view spoken instruction groups, scores sampled responses with semantic preference, rule-verifiable correctness, cross-acoustic invariance, and adaptive reasoning rewards, and optimizes the model with Cross-Acoustic Group Relative Policy Optimization (CA-GRPO). The resulting objective moves SpeechLM alignment beyond teacher imitation toward robust reasoning policies that remain stable across realistic spoken realizations. Experiments with recent SpeechLM baselines, disfluency robustness, spoken QA, audio reasoning, and speech-to-text translation show the largest gains on reasoning-heavy and acoustically perturbed spoken prompts.

We present SetDiff, a geometry-grounded multi-view diffusion framework that enhances novel-view renderings produced by 3D Gaussian Splatting. Our method integrates explicit 3D priors, pixel-aligned coordinate maps and pose-aware Plucker ray embeddings, into a set-based diffusion model capable of jointly processing variable numbers of reference and target views. This formulation enables robust occlusion handling, reduces hallucinations under low-signal conditions, and improves photometric fidelity in visual content restoration. A unified set mixer performs global token-level attention across all input views, supporting scalable multi-camera enhancement while maintaining computational efficiency through latent-space supervision and selective decoding. Extensive experiments on EUVS, Para-Lane, nuScenes, and DL3DV demonstrate significant gains in perceptual fidelity, structural similarity, and robustness under severe extrapolation. SetDiff establishes a state-of-the-art diffusion-based solution for realistic and reliable novel-view synthesis in autonomous driving scenarios.

Large language models are increasingly used to simulate human participants in social and behavioral studies, yet static persona prompting typically maps a participant profile and an experimental scenario directly to a response, entangling stable dispositions with situation-specific interpretations. To address this limitation, we introduce $\textbf{SPIN}$, a cognitive-affective personality system-inspired inference pipeline for social judgment and decision alignment. Specifically, SPIN implements this structured inference process through three zero-shot LLM calls that compile a task-blind participant core, elicit condition-specific cognitive-affective states, and read out decisions from those states, thereby reusing stable personality structure while routing each trial-specific response through an explicit state representation. We evaluate SPIN on two reconstructed social-psychological study families spanning uncertainty reasoning and pluralistic ignorance, across four base LLMs. Compared with blank, demographic, narrative, and chain-of-thought prompt variants, SPIN consistently delivers the strongest overall alignment performance across base LLMs and study families. Ablations and state analyses further show that both personality compilation and structured state elicitation contribute to the gains, and that the elicited states shift interpretably across informational and normative conditions. These results suggest that structured personality-state inference can improve benchmark-level behavioral alignment beyond richer persona descriptions or generic multi-step reasoning.

Gaze following requires both scene understanding and gaze reasoning to localize the gaze target of an in-scene person. Recently, vision foundation models (VFMs) have demonstrated strong performance on this task, enabling simpler architectures while outperforming prior methods. However, we observe a key limitation of VFM-based approaches: while VFMs substantially improve scene understanding, they contribute little to gaze reasoning. As a result, existing methods often rely on semantically salient objects rather than true gaze cues, leading to degraded performance when targets are not salient. To address this, we propose a novel training mechanism to enhance gaze reasoning in VFMs for gaze following. Our method includes: (1) a head-conditioned local LoRA, which enables localized adaptation to preserve scene token learning while improving head token learning for gaze reasoning; and (2) an out-of-cone penalty, which injects gaze cues into head tokens while aligning them with scene tokens. Experiments on the GazeFollow and VAT datasets demonstrate that our method achieves state-of-the-art performance, with particularly strong improvements when gaze targets are not semantically salient. Our findings offer valuable insights for advancing future gaze following research. We will release the code once the paper is accepted.

While agentic frameworks have advanced repository-level code localization, their reliance on static snapshots often leads to a $\textit{traceability gap}$—the loss of implicit links between issue symptoms and code implementation. To bridge this information deficit, we introduce $\textbf{T}$raceability $\textbf{A}$ugmented $\textbf{Co}$de Localization (TACO), a framework that recovers retrievable and usable traceability from repository evolution to guide localization agents. TACO offline crystallizes historical pull requests into a dual-index knowledge base through a prompt auto-tuning technique, capturing high-value semantic hints and architectural rationales. In the online phase, TACO employs a synergistic dual-track retrieval workflow, cross-validated by an arbiter and aligned temporally to handle version drift. Extensive evaluations on SWE-bench Lite and SWE-bench Verified across five state-of-the-art agentic baselines demonstrate that TACO significantly improves exact-match localization accuracy (Acc@1) by an average of 11.29\%, 13.20\%, and 12.87\% at the file, module, and function levels, respectively. Moreover, TACO significantly streamlines the exploration process, achieving an average reduction of 29.2\% in token usage and 30.4\% in monetary costs, with minimal offline maintenance overhead.

Although score-based generative models (SGMs) have achieved remarkable success in real-world sample generation tasks, it is still far from sufficient to understand them from a mathematical perspective. In this paper, we aim to provide enhanced convergence guarantees for two existing SGMs in $\mathcal{W}_2$-distance beyond log-concavity. More precisely, we develop a novel framework of error analysis for Euler-Maruyama (EM) and Poisson midpoint (PM) time discretization schemes for SGMs. Under a non-log-concavity condition, we show that $\tilde{\mathcal{O}}(\sqrt{d}/\epsilon)$ iterations suffice to approximate the target distributions in $\epsilon$-accuracy for the classical EM-based SGM, considerably improving upon the existing iteration complexity $\tilde{\mathcal{O}}(d/\epsilon^2)$. Notably, the novel framework of error analysis enables us to establish an $\tilde{\mathcal{O}}(\sqrt{d}/\epsilon^{2/3})$ iteration complexity for the PM-based SGM in $\epsilon$-accuracy, significantly outperforming current state-of-the-art Wasserstein convergence guarantees for SDE-based diffusion samplers.

Large language model (LLM) agents typically perform function calling by retrieving relevant tools and reinserting their specifications as textual prompts at inference time. Although retrieval reduces the number of candidate tools, selected tool specifications must still be repeatedly re-encoded, incurring substantial latency and memory overhead, especially in on-device environments. We argue that this inefficiency stems from treating tool knowledge as transient text rather than reusable execution memory. To address this, we propose EngramState, a state-centric framework that compiles tool specifications offline into reusable recurrent state priors and directly loads them at runtime for query-only inference. EngramState combines retention-aware state construction, channel-wise state compression, a lightweight same-backbone state retriever, and feedback-driven state routing. On the DroidCall benchmark with an RWKV-7 1.5B backbone, EngramState reduces prompt tokens from 985 to 22 (97.8\%) under the Top-4 retrieval setting and substantially lowers time-to-first-token (TTFT) on a Galaxy S25 Ultra while maintaining or improving function-calling accuracy. Furthermore, the proposed state retriever achieves competitive retrieval quality using only 8.24\,MiB of retrieval storage. These results demonstrate that reusable recurrent states can serve as an efficient execution-time memory interface for scalable on-device function calling.

Denoising and score estimation are classically linked through Tweedie’s formula, which relates the posterior mean under Gaussian corruption to the Stein score of the noisy marginal. In this work, we extend this perspective beyond Gaussian noise to a broad class of elliptical, energy-based noise distributions, with particular emphasis on generalized Gaussian corruptions. We derive the Energy–Tweedie identity: when the denoising posterior is viewed through the lens of proper scoring rules, the path derivative of a matched, possibly non-Euclidean energy score recovers the Stein score of the noisy marginal. Thus, the familiar correspondence between Gaussian noise, posterior means, squared loss, and Tweedie’s formula is lifted to a distributional correspondence between generalized Gaussian noise, full posterior laws, Mahalanobis energy scores, and the Energy–Tweedie identity. Among its consequences, this identity gives a posterior-sample-based route to score estimation, yields a principled criterion for noise-parameter calibration, and supplies a score-based perspective on recent diffusion-style generative methods trained with scoring rules.

Deep generative models, particularly Flow Matching frameworks, have emerged as powerful tools for synthesizing complex physical systems. However, standard architectures remain purely statistically driven and agnostic to the underlying governing equations, often violating fundamental physical constraints during inference. Existing zero-shot approaches designed to enforce these constraints typically rely on rigid algebraic projections. While effective for sparse boundary conditions, applying such discrete geometric corrections to dense, full-domain PDEs disrupts the probability path, pulling samples off the learned manifold and introducing intractable computational overhead. To address these challenges, we introduce Energy-Guided Transport, a novel training-free framework that formulates non-linear physical constraints as a smooth potential energy landscape. Rather than interrupting the ODE integration, our method applies a continuous dissipative force directly to the intermediate generative states, seamlessly steering them toward the physical manifold. By evaluating this force implicitly via reverse-mode automatic differentiation, our framework preserves the flow's natural dynamics while maintaining an efficient $\mathcal{O}(N)$ algorithmic complexity. We extensively evaluate our framework across various 1D and 2D physical systems, including the chaotic Navier-Stokes equations. Our method achieves stable generation without structural divergence, significantly improves the stability-accuracy trade-off compared to state-of-the-art methods, and substantially accelerates inference with respect to exact projection strategies. Code available at: \url{https://anonymous.4open.science/r/Projection-free-energy-guided-flow-matching-A302}.

Structure-informed protein representation learning is essential for effective protein function annotation and $\textit{de novo}$ design. With the rapid progress of protein structure prediction and experimental structure determination, a central challenge in the field is shifting from merely obtaining structural models to reliably leveraging predicted or measured structures for downstream learning. However, both crystal and AlphaFold-like predicted structures can contain structural uncertainty, which can bias local neighborhood construction and make robust protein representation learning challenging. To address these issues, we propose a novel equivariant Transformer-State Space Model (SSM) hybrid framework, termed $E^3$former, designed for efficient and robust protein representation. Our approach uses energy function-based receptive fields to construct proximity graphs that adaptively stabilize neighborhood selection under structural deviations, and incorporates an equivariant high-tensor-elastic selective SSM within the transformer architecture. These components allow the model to adapt to complex geometric interactions and extract structural features with a higher signal-to-noise ratio. Empirical results demonstrate that our model outperforms existing methods in structure-intensive tasks, such as inverse folding and binding site prediction, particularly when using predicted structures, owing to its enhanced tolerance to data deviation and structural uncertainty. Our approach offers a novel perspective for conducting biological function research and drug discovery using imperfect but increasingly available protein structure data. Our code is available on \url{https://anonymous.4open.science/r/E3former-207E}.

Real-world endoscopic diagnosis unfolds across native video, depending on continuous traversal of gastrointestinal organs where a single positive finding triggers a multi-minute reasoning chain over location, morphology, severity, and intervention. Existing endoscopy multimodal benchmarks evaluate only static images or single-disease videos that fail to capture the temporal and clinical reasoning demands of real-world endoscopy reporting. We introduce EndoSCOP-V, a video-grounded multi-turn evaluation framework for long-horizon clinical reasoning in endoscopy. The benchmark contains 1,307 real-world endoscopy and colonoscopy videos (63.2 h; 97% 1080p; mean ~174 s) — the time-scale at which endoscopists actually reason about a finding. The corpus is paired with 20,275 question–answer turns deterministically instantiated from clinician-filled structured endoscopy-reporting EHRs, avoiding synthetic or LLM-rephrased per-case annotations. Questions are derived from 1,033 clinician-reviewed templates spanning a clinically aligned 9-task × 30-sub-task reporting taxonomy across 54 World Endoscopy Organization (WEO) MST 3.0 diseases at ~18 templates per disease, substantially deeper per-disease examination than prior endoscopy benchmarks. To mirror real-world case mix, 22% of procedures carry co-existing pathologies, up to six per case. To model the hierarchical and conditional nature of clinical reporting, EndoSCOP-V introduces a Markovian context-pruned oracle-forcing protocol that evaluates multi-turn dialogues turn-by-turn. By injecting the gold answer before each subsequent turn, the protocol isolates per-turn reasoning from cascading dialogue errors and enables fine-grained analysis of clinical reasoning capabilities. Clinician-marked key-frame anchors further separate temporal-localization from frame-level comprehension failures. A panel of 15 board-certified endoscopists anchors a 78% strict-accuracy baseline on a 1,500-question audit. Across 14 open-weight LMMs, the best 27B-class model reaches 59.8% strict accuracy, falling to 53.4% once format-trivial disease-rejection items are excluded. On a between-subjects spot-check, the best of 3 frontier proprietary models trails the clinician panel by 23 pp on strict accuracy. Sub-task analysis reveals consistent gaps in quantitative measurement, multi-variable grading, and intervention reasoning on long-horizon video, providing a clinically grounded benchmark for evaluating multimodal reasoning in endoscopy.

Neuro-symbolic systems for sequence classification couple a neural perception network with a finite automaton over abstract events. Certifying their trajectory-level decisions under input perturbations is critical for safety-sensitive deployment. We address the open problem of end-to-end formal robustness certification for such Neurosymbolic Automata (NeSyA) under norm-bounded input perturbations. Naive layerwise linear bound propagation fails on this composition: per-step relaxation slack compounds through the temporal recurrence (interval explosion), and the standard log-space transition score subtracts two log-sum-exp terms over overlapping coordinate sets that independent relaxations cannot tighten. To address both, we introduce contrastive logit-gap (CLG), a reparameterization whose two log-sum-exp heads act on disjoint coordinates and admit substantially tighter linear bounds, coupled with a fused temporal operator that yields corner-tight bounds from a single linearization. On NeSyA sequence benchmarks, our formulation scales significantly beyond recursive baselines, maintaining tight safety certificates at long horizons while reducing verification wall time by up to ${\sim}6{\times}$. To our knowledge, this is the first end-to-end formal verification framework for temporal neuro-symbolic systems.

Learning identifiable representations in deep generative models remains a fundamental challenge, particularly for sequential data with regime-switching dynamics. Existing approaches establish identifiability under restrictive assumptions, such as stationarity or limited emission models, and typically rely on variational autoencoder (VAE) estimators, which introduce approximation gaps that limit the recovery of the latent structure. In this work, we address both the theoretical and practical limitations of this setting. First, we establish identifiability of a broad class of recurrent nonlinear switching dynamical systems under flexible assumptions, significantly extending prior results. Second, we introduce $\Omega$SDS, a flow-based estimator that enables exact likelihood optimization using expectation-maximisation. Through empirical validation on both synthetic and real-world data, our results demonstrate that $\Omega$SDS achieves improved disentanglement compared to VAE-based estimators and more accurate forecasting of underlying dynamics.