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Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

NeurIPS 2026

Playing Markov Games Without Observing Payoffs

Daniel Ablin, Alon Peled-Cohen

Optimization under uncertainty is a fundamental problem in learning and decision-making, particularly in multi-agent systems. Previously, Feldman, Kalai, and Tennenholtz [2010] demonstrated the ability to efficiently compete in repeated symmetric two-player matrix games without observing payoffs, as long as the opponent’s actions are observed. Extending this capability to the Markovian setting remains an open problem. In this paper, we introduce and formalize a new class of zero-sum symmetric Markov games, which extends the notion of symmetry from matrix games to the Markovian setting. We prove that a learner observing only the opponent's action sequence, without access to payoff information can successfully compete against an adversary possessing complete knowledge of the game. We formalize three distinct notions of symmetry in this domain and reveal a surprising structural hierarchy: the most holistic definitions of symmetry impose restrictive constraints that actually simplify the learning landscape, whereas the ``simplest'' definition represents the most general and challenging setting. We provide polynomial-time algorithms for all three settings that achieve sublinear regret. Crucially, we demonstrate that despite the complex Markovian dynamics, a simple strategy of locally mimicking the opponent's actions suffices to guarantee robustness. This finding significantly broadens the class of games where robust learning is possible under severe informational disadvantage, proving that knowledge of the transition laws is not required to force a draw.

NeurIPS 2026

Plausible Biomolecular Structure Prediction via Physics-informed Reinforcement Learning

Tai Dang, Hieu Tran, Long-Hung Pham, Sang Truong, Edward A Pham, Jeffrey S Glenn, Thang Luong

The latest advances of diffusion models in biomolecular structure prediction are still based on distance-based supervised training without adequately accounting for physically plausible interactions needed for real-world drug discovery. We introduce $\mathit{\boldsymbol{\pi}\text{-Fold}}$, a novel physics-informed policy-based folding approach that augments reinforcement learning with physics-aware scoring functions for training biomolecular diffusion models. Specifically, we fine-tune AlphaFold3-based models using Group Relative Policy Optimization and explicit physics-based rewards such as Rosetta all-atom energy, AutoDock Vina binding affinity, and ligand strain energy. Crucially, we demonstrate that these physics-based priors translate to enhanced performance on critical downstream prediction tasks. $\mathit{\boldsymbol{\pi}\text{-Fold}}$ achieves state-of-the-art results on SKEMPI for protein--protein mutation effects, CASP16 and OpenFE for protein--ligand binding affinities, Run-N-Poses for ligand geometric validity and interface quality across diverse structural fidelity metrics. On FoldBench, $\mathit{\boldsymbol{\pi}\text{-Fold}}$ achieves the best overall MolProbity score of 1.30, halving steric clashes and nearly eliminating rotamer outliers. Human evaluation further confirms that $\mathit{\boldsymbol{\pi}\text{-Fold}}$ generates structures that are meaningful for downstream drug discovery applications. Our code is available at \href{https://anonymous.4open.science/r/pifold-45B8/README.md}{github.com/anonymous/pifold}.

NeurIPS 2026

Plausibility Is Not Prediction: Contrastive Evidence for LLM-Based Cellular Perturbation Reasoning

Xinyu Yuan, Xixian Liu, Jianan Zhao, Ya Shi Zhang, Hongyu Guo, Jian Tang

Perturbation experiments are central to understanding cellular mechanisms, but remain costly and sparse, motivating prediction of gene expression responses for unobserved conditions. A promising recent direction leverages large language models (LLMs) as “virtual cell” simulators—using stepwise, knowledge-grounded mechanistic reasoning to infer differential expression—pointing toward an interpretable, knowledge-driven paradigm that transcends purely data-driven approaches. However, we find that plausibility is not prediction: despite producing biologically plausible explanations, these methods fail to capture perturbation-specific effects — systematically overestimating differential expression, often underperforming a simple gene‑frequency baseline in aggregate evaluations, and collapsing to chance-level performance at the per-gene level. This reveals a reliance on intrinsic gene response tendencies rather than true perturbation reasoning. We trace this failure to how evidence is presented: existing methods evaluate perturbation–gene pairs in isolation, without exposing how related perturbations differ in their effects on the same gene. To address this limitation, we introduce **CORE** (**C**ontrastive **O**rganization of **R**elational **E**vidence), which reframes prediction as a comparison task by organizing evidence into positive and negative outcomes from related perturbations. Using a biomedical knowledge graph for evidence retrieval, CORE improves calibration and substantially boosts perturbation-specific prediction in both LLM-based and non-LLM settings: for example, on drug-perturbation data, CORE-Reasoning improves Qwen3.5-9B aggregate metrics by up to 28.6\%, while on generic perturbation data, CORE-Voting raises macro-per-gene AUROC from chance to 0.711. This highlights contrastive evidence organization as essential to reliable LLM-based perturbation reasoning.

NeurIPS 2026

Platonic Task Arithmetic

Junghwan Park, Woojin Cho

When distinct pre-trained models are specialized for the same task, they often converge to nearly equivalent functional behaviors, while the underlying parameter-space changes share no common coordinate system. Existing approaches that compose such changes arithmetically are therefore confined to a single model, leaving task knowledge stranded inside the network that acquired it. Drawing on Plato's allegory of the cave, we hypothesize that these model-specific updates are shadows cast by a shared, model-agnostic object that governs how task specialization reshapes a model's behavior, and we refer to this object as the platonic task vector. To make this view operational, we introduce Universal Task Descriptors, matrices whose shape is fixed independently of architecture or embedding dimension and that capture a task's functional effect in a model-transferable form. Universal Task Descriptors admit addition, negation, and analogy in closed form, and a lightweight realization step then transfers any composed descriptor into a chosen target model. Experiments across a range of models and tasks indicate that this pipeline transfers and composes task knowledge across heterogeneous models.

NeurIPS 2026

Platonic Representations in the Human Brain: Unsupervised Recovery of Universal Geometry

Pablo Marcos Manchón, Rishi Jha, Lluís Fuentemilla

The Strong Platonic Representation Hypothesis suggests that representational convergence in artificial neural networks can be harnessed constructively: embeddings can be translated across models through a universal latent space without paired data. We ask whether an analogous geometry can be recovered across human brains. Using fMRI data from the Natural Scenes Dataset, we propose a self-supervised encoder that learns subject-specific embeddings from brain data alone by exploiting repeated stimulus presentations. We show that these independently learned spaces can be translated across subjects using unsupervised orthogonal rotations, without paired cross-subject samples or intermediate model representations. Synchronizing pairwise rotations into a single shared latent space further improves cross-subject retrieval, indicating that subject-specific spaces are mutually compatible with a common coordinate system. These results provide evidence for a shared neural geometry in the human visual cortex: subject-specific fMRI representations are approximately isometric across individuals and can be translated through purely geometric transformations.

NeurIPS 2026

Plan4D: Generative Plannable 4D Worlds

Hangwei Zhang, Rujing Dang, Ruimin Lin, Jiahong Wu, Xiangxiang Chu, Chuanxia Zheng

Most world models for future prediction prioritize aestetic quality of generated videos, but offer only weak physical grounding and limited geometric consistency over time, due to the lack of explicit 4D modeling. In this paper, we introduce plan4d, a possible next milestone towards building a generative plannable 4D world model that predicts future RGB videos together with dense 4D geometry, including depth, cameras, point clouds, and scene flow from an observed scene context and text-specified actions. Since training data with paired actions and dense 4D annotations is inherently scarce, plan4d leverages the complementary priors of foundational video generators and 3D reconstructors. Specifically, we propose a model-stitching architecture that couples Depth Anything 3 with LongCat-Video and augments it with an auxiliary DPT head for scene-flow prediction. The resulting model allows us to plan and generate complex, long-horizon, and fine-grained actions in a dynamic 3D scenes, while substantially improving both visual fidelity and geometric accuracy of the generated future videos. Extensive experiments on both synthetic and real-world datasets demonstrate that plan4d achieves state-of-the-art performance in generative 4D modeling of environmental reactions.

NeurIPS 2026

Plan2Sense: Open-World Task Planning in Epistemic States via Interleaved Ontic and Sensing Actions

Xiaotian Liu, Armin Toroghi, Jiazhou Liang, Ali Pesaranghader, Scott Sanner

Open-world task planning requires not only generating feasible plans but also acquiring the knowledge needed to execute them. Existing task planners for egocentric agents either attempt to exhaustively gather information from the environment or generate plans under prior assumptions of the world and replan as new observations arrive. Both strategies are impractical in complex environments and can potentially expose agents to undesirable or unsafe situations. To enable effective planning in open-world settings, we propose Plan2Sense, a framework that grounds planning in the agent’s epistemic state to determine what information is required for plan feasibility and how to acquire it. We formalize decision-making as a three-valued first-order Knowledge-State Markov Decision Process (KS-MDP) and develop a knowledge state goal regression algorithm KSR to construct sensing-aware conditional plans in the open-world setting. Our formulation supports sensing actions for both predicate disambiguation and knowledge acquisition via object binding without assuming a fully known initial state or a closed set of predefined objects. Across three open-world egocentric benchmarks requiring active knowledge acquisition, \textsc{Plan2Sense} achieves higher success rates and improved efficiency over existing baselines while consistently avoiding undesirable and potentially unsafe states.

NeurIPS 2026

Pixels to Tokens: Token Space Efficient Active Learning for Low-Budget Semantic Segmentation

Utku Cicek, Mahip Singh, Ziyao Shang, Kimathi Kaai, C Thomas, Pablo Guerrero, Alexander Wong, Sirisha Rambhatla

Semantic segmentation typically requires dense pixel-level annotations, creating a prohibitive bottleneck for budget-constrained applications. While foundation models and vision transformers (ViTs) have redefined visual representations, current active learning (AL) strategies for these backbones largely operate at the patch or region level, where annotation requirements remain high. Extending ViT-based AL to the pixel-level, low-budget regime is uniquely challenging: with as little as one pixel query per image, a method must simultaneously identify the most informative locations and train a reliable decoder head from an extremely sparse signal. We introduce TEAL (Token-space Efficient Active Learning), the first pixel-level AL framework built around ViT representations. TEAL uses a frozen DINOv3 backbone and performs diversity-based selection directly on its native token lattice, avoiding the artifacts introduced by interpolating token embeddings to dense pixel resolutions. Our framework applies a two-level MaxHerding strategy over multi-layer token descriptors to select representative candidates, followed by margin-based uncertainty refinement on a finer decoder grid. Across CamVid, Cityscapes, ADE20K, and Pascal Context, TEAL consistently outperforms previous baselines under extreme label scarcity. After 10 rounds of 1-pixel-per-image queries, TEAL improves over the strongest baseline by up to $+\textbf{14.65}$ mIoU on CamVid and $+\textbf{23.34}$ mIoU on Pascal Context, showing that ViT token spaces provide an effective geometry for extremely-low-budget active segmentation.

NeurIPS 2026

PixelPonder: Dynamic Patch Adaptation for Enhanced Multi-Conditional Text-to-Image Generation

Yanjie Pan, Qingdong He, Zhengkai Jiang, Pengcheng Xu, Chaoyi Wang, Jinlong Peng, Haoxuan Wang, Yun Cao, Zhenye Gan, Mingmin Chi, Bo Peng, Yabiao Wang

Recent advances in diffusion-based text-to-image generation have demonstrated promising results through visual condition control. However, existing ControlNet-like methods struggle with compositional visual conditioning - simultaneously preserving semantic fidelity across multiple het erogeneous control signals while maintaining high visual quality, where they employ separate control branches that often introduce redundant guidance during the denoising process, leading to structural distortions and artifacts in generated images. To address this issue, we present PixelPonder, a novel unified control framework, which allows for effective control of multiple visual conditions under a single control structure. Specifically, we design a patch-level adaptive condition selection mechanism that dynamically prioritizes spatially relevant control signals at the subregion level, enabling precise local guidance without global interference. Additionally, a time-aware control injection 017 scheme is deployed to modulate condition influence according to denoising timesteps, progressively transitioning from structural preservation to texture refinement and utilizing the control information from different categories to promote finer image generation. Extensive experiments demonstrate that PixelPonder surpasses previous methods across different benchmark datasets, showing superior improvement in spatial alignment accuracy while maintaining high textual semantic consistency.

NeurIPS 2026

PixelDense: Dense Prediction as Representation Alignment for Pixel Diffusion

Lehan Yang, Daiqing Qi, Wenhao Zhang, Avery Li, Yiqing Yang, Yifan Li, Yu Kong, Haitian Zheng, Zhifei Zhang, Zhe Lin, Varun Jampani, Sheng Li

Representation alignment (REPA) accelerates diffusion transformer training, but its alignment targets are almost exclusively semantic encoders such as DINOv2 and CLIP. Recent analysis points to spatial structure, not global semantics, as the carrier of the alignment effect, yet dense-prediction foundation models trained to predict that structure remain overlooked as REPA targets. In pixel-space diffusion, SAM2, Depth Anything v2, and Metric3D v2 each outperform the DINOv2-only GenEval baseline, with the two geometric teachers leading the segmentation teacher. A flat sum of all four teachers, however, lands below the best single geometric teacher, semantic teachers reward viewpoint invariance and instance identity while geometric teachers reward metric layout and surface orientation, and forcing both kinds of gradients through one denoiser projection collapses them into a shared subspace. We introduce PixelDense, which routes DINOv2 and SAM2 through a semantic projection stream, routes Depth Anything v2 and Metric3D v2 through a geometric projection stream, and adds a weight-space orthogonality penalty that keeps the two streams in disjoint subspaces. All four teachers are frozen during training and dropped at inference. Applied to PixelGen and DeCo with a single recipe, PixelDense improves GenEval, DPG-Bench, and HPS v2.1, raises PixelGen-XXL's GenEval Overall from $0.7927$ to $0.8093$, and beats every single-teacher and unfactored multi-teacher variant. In partial-noise reconstruction, independent panoptic, depth, and surface-normal probes show up to 53.1% PQ gain and 36.0% depth AbsRel reduction at $\tau=0.5$ across COCO and Flickr30K. From random initialization, PixelDense also reaches the baseline's peak GenEval $1.23\times$ faster.

NeurIPS 2026

PixFoundation 2.0: Do Video Multi-Modal LLMs Use Motion in Visual Grounding?

Mennatullah Siam

Multi-modal large language models (MLLMs) have shown impressive generalization across tasks using images and text modalities. While their extension to video has enabled tasks such as video question answering and video captioning, their pixel-level visual grounding abilities are less studied. In this work, we raise the pertinent question of whether motion is used in pixel-level visual grounding and whether video MLLMs can segment objects based on natural language expressions describing their motion patterns. We identify the shortcomings in the current benchmarks, where we show that a single frame can often suffice for capturing the referring expression without any temporal reasoning. To address this, we introduce novel motion-centric probing, particularly designed for the visual grounding task, to study video MLLMs' ability to identify true motion from a fake one and their ability to grasp the ordering of motion. Consequently, we introduce MoCentric-Bench, a motion-centric benchmark designed to evaluate video MLLMs based on their ability to capture the interaction between motion and language, rather than relying primarily on static cues. We further establish strong single-image baselines that either match or surpass prior methods. Finally, we explore a simple motion-centric adaptation that provides state-of-the-art performance on our MoCentric-Bench. Code is available at \url{https://anonymous.4open.science/r/PixFoundation2MoCentricBenchFin/}.

NeurIPS 2026

PithTrain: A Compact and Agent-Native MoE Training System

Ruihang Lai, Hao Kang, Haozhan Tang, Akaash Raghunathan Parthasarathy, Zichun Yu, Junru Shao, Todd Mowry, Chenyan Xiong, Tianqi Chen

Mixture-of-Experts (MoE) has become the dominant architecture for frontier language models. To meet this demand, production frameworks have built optimized MoE training stacks over years of engineering effort. Yet evolving these stacks for new architectures and system optimizations remains expensive. With the rise of AI coding agents, they could automate parts of training-framework development and accelerate this evolution. But applying them to these existing frameworks carries hidden costs, invisible to today's throughput-only evaluations. We name this missing dimension agent-task efficiency (ATE): the cost of using coding agents to understand, operate, and extend a framework. Grounded in four agent-native design principles, we build PithTrain, a compact, agent-native MoE training framework. We further introduce ATE-Bench, covering real-world training-framework tasks. Our evaluation shows PithTrain matches the throughput of production frameworks, and on ATE-Bench, PithTrain enables higher agent-task efficiency, with up to 62% fewer Agent Turns and 64% less Active GPU Time.

NeurIPS 2026

PitchBench: Measuring Pitch Hearing in Audio-Language Models

Milan Liessens Dujardin, Song-Ze Yu, Craver C Thomas-Smith, David Chan, Karina Nguyen

Audio-language models (ALMs) are increasingly used in real-world applications that require understanding music, from music tutoring and transcription to captioning, recommendation systems, and music production. More broadly, they are becoming an important component of multimodal AI systems that must reason from sensory input rather than text alone. This makes reliable musical perception a critical prerequisite: if a model cannot accurately hear the structure of sound, it cannot be trusted to reason about, teach, transcribe, or act on audio in the real world. Yet existing benchmarks rarely assess one of the most fundamental musical abilities underlying such perception: pitch hearing. Current evaluations tend to probe pitch hearing only indirectly, through higher-level tasks and often in multiple-choice formats, leaving open how reliably ALMs identify fine-grained pitch across instruments, acoustic conditions, and response formats. We introduce PitchBench, an evaluation suite that systematically measures pitch hearing in ALMs. PitchBench comprises 28 experiments spanning absolute and relative pitch perception within sequences and chords, while varying loudness, note duration, sound source, time stretching, background noise, and other acoustic conditions. Tasks range from identifying individual pitches in isolation to tracking a melodic line within a four-part musical texture. Evaluating frontier ALMs, we find that pitch hearing remains highly unreliable: models perform consistently poorly across settings, with accuracy varying sharply by sound source, note duration, and notation format. Current ALMs do not yet possess stable pitch perception, even for controlled synthetic and instrumental stimuli. Alongside the benchmark, we release PitchBench as a Python package containing the evaluation data and data generation tools to support future work on pitch-aware audio-language modeling.

NeurIPS 2026

PipeFSDP: Efficient Pipeline Parallel under Fully Sharded Data Parallel for Large Language Model Training

Xinglin Pan, Mingji Han, Penghao Zhao, Lin Zheng, Rayying, Key, Shaohuai Shi, Xiaowen Chu

It is now a common practice to use PyTorch’s official Fully Sharded Data Parallel (FSDP) framework to train large language models (LLMs) on large-scale GPU clusters, typically together with pipeline parallelism (PP) to achieve better performance. However, existing popular systems like TorchTitan with FSDP and PP are inefficient due to the large bubbles and peak memory footprint caused by suboptimal task scheduling. In this paper, we propose PipeFSDP, an efficient training framework for FSDP combined with PP that alleviates pipeline bubbles and resource contention, thus improve training efficiency. Specifically, we design four fine-grained optimizations: dynamic context-aware prefetching, communication contention mitigation, redundant reshard elimination, and communication order optimization. In addition, we develop a heuristic model that selects the FSDP and PP degrees based on the given hardware and model configurations, thereby enhancing the practicality of PipeFSDP. Empirical evaluations on both dense and sparse LLMs (Llama3, Qwen3, and Qwen3-MoE) across 32-GPU and 64-GPU clusters demonstrate that PipeFSDP significantly outperforms the Torchtitan system by up to 1.28$\times$ in Model FLOPs Utilization (MFU) and 1.87$\times$ in memory consumption.

NeurIPS 2026

Pion: A Spectrum-Preserving Optimizer via Orthogonal Equivalence Transformation

Kexuan Shi, Hanxuan Li, Zeju Qiu, Yandong Wen, Simon Buchholz, Weiyang Liu

We introduce Pion, a spectrum-preserving optimizer for large language model (LLM) training based on orthogonal equivalence transformation. Unlike additive optimizers such as Adam and Muon, Pion updates each weight matrix through left and right orthogonal transformations, preserving its singular values throughout training. This yields an optimization mechanism that modulates the geometry of weight matrices while keeping their spectral norm fixed. We derive the Pion update rule, systematically examine its design choices, and analyze its convergence behavior along with several key properties. Empirical results show that Pion offers a stable and competitive alternative to standard optimizers for both LLM pretraining and finetuning.

NeurIPS 2026

PieArena: Ranking and Profiling Language Agents in Realistic Negotiation Scenarios

Chris Zhu, Sasha Cui, Will S Dufallo, Runzhi Jin, Zhen Xu, Linjun Zhang, Daylian M Cain

We present an in-depth evaluation of LLMs' ability to negotiate, a central business task requiring strategic reasoning, theory of mind, and economic value creation. To do so, we introduce **PieArena**, a large-scale negotiation benchmark grounded in multi-agent interactions over realistic scenarios adapted from MBA negotiation courses at an elite business school. We evaluate language agents across three pairing regimes: mirror-play, cross-play, and human–LM play. We develop a ranking model for continuous negotiation payoffs that yields order-invariant, uncertainty-quantified leaderboards while correcting for systematic experimental asymmetries. We further study the effects of joint-intentionality agentic scaffolding and find asymmetric gains, with large improvements for mid- and lower-tier LMs and diminishing returns for frontier LMs. As calibration anchors, we collect human–human and human–LM negotiation data from trained business school students, finding that a representative frontier language agent (GPT-5) matches or exceeds this human baseline in our evaluation settings. Beyond deal outcomes, PieArena provides a multi-dimensional behavioral profile that reveals cross-model heterogeneity in instruction compliance, computation accuracy, as well as judge-assessed deception and reputation, illustrating the value of evaluation beyond outcome-only leaderboards.

NeurIPS 2026

PickMoment: Continuous-Time Single-Image-to-Video via Learning Deblurring and Blur-to-Video

Junseong Shin, Hyeonsu Jo, Daehyun Kim, Tae Hyun Kim

Motion blur arises from the temporal integration of a continuous sharp signal over a finite exposure window, yet existing learning-based methods sidestep this physical model and predict only the sharp signal itself: most single-image deblurring methods recover a single frame at the exposure center, while blur-to-video methods predict a fixed set of frames. We introduce PickMoment, a continuous-time reformulation that directly learns the interval-mean blur over arbitrary sub-intervals of the exposure with a single deterministic model. Drawing an analogy to MeanFlow's average-velocity formulation, we train the model with three supervisions derived from the blur integral: an empirical reconstruction loss from available subframes, an additivity loss that enforces self-consistency across overlapping sub-intervals, and a sharp-frame loss anchored at the zero-interval limit. A single trained model unifies single-image deblurring, blur-to-video generation, and continuous-time pick-a-moment recovery as different queries to the same network, with no separate training for each task. Our PickMoment achieves state-of-the-art performance among generative-based deblurring methods on GoPro and HIDE while competitive against restoration-based methods on RealBlur, and the highest per-frame fidelity on GoPro-7 blur-to-video, all in a single forward pass without iterative sampling.

NeurIPS 2026

PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion

Yifan Lu, Qi Wu, Jay Zhangjie Wu, ZiAn Wang, Huan Ling, Sanja Fidler, Xuanchi Ren

Most practical high-resolution text-to-image systems rely on latent diffusion models, where generation is performed in a compact latent space and a decoder maps latents back to pixels. Yet the latent-to-pixel decoder in this pipeline remains primarily reconstruction-oriented: as the sole pathway from latents to pixels, it is optimized to invert an encoder rather than to synthesize high-resolution details, and becomes increasingly costly in megapixel pipelines. This gap calls for a more expressive and efficient decoding paradigm. Motivated by recent progress in scalable pixel-space diffusion, we introduce **PiD**, a **Pi**xel diffusion **D**ecoder that reformulates latent decoding as conditional pixel diffusion, unifying decoding and upsampling into one generative module. By denoising directly in high-resolution pixel space, PiD synthesizes $4\times$and even $8\times$ upscaled images with low latency. For latent conditioning, a lightweight sigma-aware adapter injects noise-corrupted latents into the backbone, enabling PiD to handle partially denoised states and terminate the base diffusion process early. To further improve efficiency, we distill the decoder using DMD2, reducing inference to just $4$ steps. PiD applies to both conventional VAE latents and semantic latents (e.g., SigLIP, DINOv2) used in recent RAE-based models. Across multiple latent spaces and base generators, PiD improves visual fidelity over conventional decode-then-upsample cascades while reducing memory and latency, decoding latents of $512{\times}512$ images into $2048{\times}2048$ pixels in under $1$ second with $13$ GB peak memory on a consumer RTX 5090, and as fast as $210$ ms on a data-center GPU, about $6{\times}$ faster than cascaded diffusion-based super-resolution pipelines.

NeurIPS 2026

PiCA: Pivot-Based Credit Assignment For Search Agentic Reinforcement Learning

Dongyi Liu, Yifan Niu, Qinwen Wang, Han Xiao, Jia Li

Large language model (LLM)-based search agents trained with reinforcement learning (RL) have significantly improved the performance of knowledge-intensive tasks. However, existing methods encounter critical challenges in long-horizon credit assignment: (i) Reward Sparsity, where models receive only outcome feedback without step-level guidance to differentiate action quality; (ii) Isolated Credit, where credit is assigned to steps independently, failing to capture sequential dependencies; and (iii) Distributional Shift, where rewards are estimated on templates that deviate from the model’s natural generative distribution. To address these issues, we propose Pivot-Based Credit Assignment (PiCA), a novel step reward mechanism that reformulates the search trajectory as a sequential process of cumulative information gain. Unlike prior methods that reward steps in isolation, PiCA defines process rewards as success probabilities dependent on the historical context. This approach identifies pivot steps (critical milestones in the search) as information peaks that significantly boost the likelihood of a correct final answer. By anchoring these step rewards to the final task objective, PiCA provides dense, pivot-aware and trajectory-dependent guidance while maintaining distributional consistency. Theoretically grounded in Potential-Based Reward Shaping (PBRS), PiCA consistently outperforms existing strong baselines across seven knowledge-intensive QA benchmarks, achieving 15.2% and 2.2% improvements for 3B and 7B models.

NeurIPS 2026

Physics-Informed Video Generation via Mixture-of-Experts Latent Alignment

Cong Wang, Hanxin Zhu, Jiayi Luo, Yonglin Tian, Xiaoqian Cheng, Peiyan Tu, Xin Jin, Long Chen, Zhibo Chen

Large-scale video generation models have made remarkable progress in semantic consistency and visual quality, producing videos that are increasingly coherent and visually convincing. Nevertheless, the dynamics induced by pixel-level fitting do not naturally accommodate the regularities that govern real-world motion and interaction, resulting in persistent shortcomings in physical plausibility. To address this limitation, we propose **PILA** (Physics-Informed Latent Alignment), a framework that injects physics-structured latent guidance into the frozen flow-matching dynamics of pretrained video models. Specifically, PILA first employs anchored field estimation to map frozen-generator latents into an operational physical attribute bank organized by field-proxy slots, using observable motion as a kinematic anchor for constructing less directly observed proxies. To handle the heterogeneity of real-world dynamics, PILA adopts a mixture-of-experts design over physical categories. Label-prior masked expert routing selects category-specific operator experts, whose refinements are regularized by operational residuals abstracted from physical relations. Finally, the refined proxies are fused into the physical attribute bank and decoded into a correction to the flow-matching vector field, injecting physics-aware guidance while preserving the visual prior of the pretrained backbone. With staged adapter training on Wan 2.1-1.3B and direct transfer of the learned adapter to Wan 2.2-14B, PILA achieves state-of-the-art results on VBench-2.0, VideoPhy-2, and PhyGenBench in both visual quality and benchmark-measured physical plausibility.

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