NeurIPS 2026 论文列表

The recent development of Large Language Models (LLMs) has been accompanied by an effervescence of novel ideas and methods to better optimize the loss of deep learning models. Claims from those methods are myriad: from faster convergence to removing reliance on certain hyperparameters. However, the diverse experimental protocols used to validate these claims make direct comparisons between methods challenging. This study presents a comprehensive evaluation of recent optimization techniques across standardized LLM pretraining scenarios, systematically varying model size, batch size, and training duration. Through careful tuning of each method, we provide guidance to practitioners on which optimizer is best suited for each scenario. For researchers, our work highlights promising directions for future optimization research. Finally, by releasing our code and making all experiments fully reproducible, we hope our efforts can help the development and rigorous benchmarking of future methods.

Recent advancements in Large Vision-Language Models (LVLMs) have significantly enhanced their ability to integrate visual and linguistic information, achieving near-human proficiency in tasks like object recognition, captioning, and visual question answering. However, current benchmarks typically focus on knowledge-centric evaluations that assess domain-specific expertise, often neglecting the core ability to reason about fundamental mathematical elements and visual concepts. We identify a gap in evaluating elementary-level math problems, which rely on explicit visual dependencies-requiring models to discern, integrate, and reason across multiple images while incorporating commonsense knowledge, all o which are crucial for advancing toward broader AGI capabilities. To address this gap, we introduce VCBench, a comprehensive benchmark for multimodal mathematical reasoning with explicit visual dependencies. VCBench includes 1,720 problems across six cognitive domains, featuring 6,697 images (averaging 3.9 per question) to ensure multi-image reasoning. We evaluate 26 state-of-the-art LVLMs on VCBench, revealing substantial performance disparities, with even the top models unable to exceed 50% accuracy. Our findings highlight the ongoing challenges in visual-mathematical integration and suggest avenues for future LVLM advancements.

Large Vision-Language Models (LVLMs) have shown remarkable potential for embodied AI, yet a critical bottleneck persists: the lack of fine-grained spatio-temporal alignment between high-level textual reasoning and low-level visual grounding. Existing benchmarks fail to recognize the critical importance of such fine-grained alignment for embodied tasks. To address this gap, we introduce ECLBench, a comprehensive benchmark designed to systematically assess spatio-temporal embodied understanding across two primary capabilities: Embodied Cognition (Object Cognition and Spatial Cognition) and Embodied Localization (Grounding and Pointing). Together, these four pillars encompass 21 specialized sub-capabilities, supported by 3,616 egocentric video clips (577,998 frames) and 12,000 meticulously curated open-ended questions. Extensive evaluations on ECLBench reveal that general LVLMs excel in semantic understanding but struggle with precise spatio-temporal grounding, while existing embodied agents often sacrifice generalization for specialization. Guided by the benchmark's findings, we develop ECLBrain, a baseline model that discretizes continuous spatial coordinates into integer tokens. This simple yet effective design bridges textual semantics and spatio-temporal vision, demonstrating a promising path toward unified embodied reasoning. ECLBench and ECLBrain together establish a rigorous, multi-dimensional standard for diagnosing current limitations and guiding future research toward spatially-aware and temporally-consistent embodied intelligence.

Machine Learning Interatomic Potentials play a fundamental role in computational chemistry and materials science, enabling applications from molecular dynamics simulations to drug design and materials discovery. While recent approaches can estimate inter-atomic forces with high precision, it remains unclear to what extent they can generalise to previously unseen molecules. Do they learn the compositional structure of chemistry, capturing how molecular fragments and their combinations determine properties, or do they primarily learn to interpolate patterns that are specific to the training examples? To address this question, we propose a benchmark consisting of four tasks that require some form of compositional generalisation. In each task, models are tested on molecules that were unseen during training, but the training data is chosen such that generalisation to the test examples should be feasible for models that learn the underlying physical principles. Our empirical analysis shows that the considered tasks are highly challenging for state-of-the-art models, with errors on out-of-distribution examples often an order of magnitude higher than on in-distribution examples, even when using foundation models that have been pre-trained on millions of molecules.

Here we introduce the Olfactory Perception (OP) benchmark, designed to assess the capability of large language models (LLMs) to reason about smell. The benchmark contains 1,010 questions across eight task categories spanning odor classification, odor primary descriptor identification, intensity and pleasantness judgments, multi-descriptor prediction, mixture similarity, olfactory receptor activation, and smell identification from real-world odor sources. Each question is presented in two prompt formats, compound names and isomeric SMILES, to evaluate the effect of molecular representations. Evaluating 21 model configurations across major model families, we find that compound-name prompts consistently outperform isomeric SMILES, with gains ranging from +3.1 to +18.9 percentage points (mean $\approx$ +7 points), suggesting current LLMs access olfactory knowledge primarily through lexical associations rather than structural molecular reasoning. The best-performing model reaches 64.4\% overall accuracy, which highlights both emerging capabilities and substantial remaining gaps in olfactory reasoning. We further evaluate a subset of the OP across 21 languages and find that aggregating predictions across languages improves olfactory prediction, with AUROC=0.86 for the best performing language ensemble model. LLMs should be able to handle olfactory and not just visual or auditory information.

Large language models can achieve strong benchmark scores without proportional gains in broader capability, but diagnosing when this occurs remains difficult. We study this gap through benchmark shadows: support-concentrated data regimes that may concentrate learning around narrow evaluation-relevant patterns while limiting broader representational development. Under fixed architecture, tokenizer, optimizer family, and training budget, we compare a coverage-expanding baseline with two support-concentrated regimes: redundant repetition and frequency-concentrated rewriting. Spectral, rank-based, and layer-wise update diagnostics reveal distinct parameter-space signatures: repetition-concentrated training is largely recoverable after later diverse training, whereas frequency-concentrated support collapse leaves more persistent footprints. Correlational analyses of open-source multimodal model families reveal analogous structural patterns that co-occur with asymmetric benchmark profiles, while a prompt de-duplication case study shows that surface redundancy alone does not induce the same regime-level effects. Benchmark scores alone are therefore insufficient to characterize capability; parameter-space diagnostics provide complementary signals about training quality, coverage, and generalization-related regime effects.

Benchmark recovery is often treated as a deployment certificate for low-bit LLMs, but thresholded controller stacks ship a different object: a frozen score-to-action contract. This paper asks a narrower acceptance question: after quantization, does that interface survive independent threshold retuning against frozen FP16 labels, the strongest cheap repair an operator would plausibly try first? We study one fixed four-action controller, one frozen train/cal/report protocol, and four instruction-tuned checkpoints: Llama-3.1-8B/70B-Instruct and Qwen-2.5-7B/72B-Instruct. In the only score-carrying regime, W4A16, GPTQ + -search stays near FP16 on MMLU yet still leaves 11.9/10.9 percentage-point false tool-trigger rates and 16.1/15.4 aggregate FCR on the two large models. Because BFCL directly labels the tool frontier, that failure already establishes the paper's main acceptance claim even if FAQ and the preservation-only retrieve/stop frontiers are ignored. Aggregate FCR is reported only as interface-wide preservation context because retrieve/stop remain preservation-only. FAQ is included only as a bounded reducibility probe under the same frozen audit target, not as a generic new quantizer or the central novelty claim. FAQ lowers tool FTR and aggregate FCR without obvious MMLU or throughput collapse, but that is secondary non-collapse evidence. The contribution is therefore a single-controller deployment-acceptance counterexample under a frozen permission set: on the retained large-model W4A16 rows, benchmark-close MMLU recovery does not certify preserved tool-trigger behavior. The accompanying contribution is the freeze-and- retune audit protocol that makes that failure measurable.

Current video foundation models, including the strongest self-supervised models such as V-JEPA2, fail to capture how humans organize social information in dynamic scenes. For example, across a range of diverse vision models tested, none were able to predict human similarity judgments to social video clips as well as a sentence embedding model of the caption text (MPNet). We show this gap in vision model performance can be closed by a compact behavioral supervisory signal. We introduce behavioral geometric supervision (BGS): a hybrid objective that constrains local and global pairwise embedding geometry to match the relational similarity structure across videos. We apply this method using a new human similarity dataset, containing 49,484 odd-one-out judgments from 250 naturalistic social video clips, and low-rank adaptation across four ViT backbones (V-JEPA 2/2.1, TimeSformer, VideoMAE, and CLIP). We find that one of the best fine-tuned models, V-JEPA 2.1, nearly triples in performance compared to the pre-trained baseline and reaches close to the noise ceiling, exceeding the strongest sentence-embedding baseline. In addition, finetuned models (i) capture unique variance in human judgments that caption-based language embeddings do not, (ii) develop interpretable social-affective attributes (valence, arousal, and dominance) despite never being trained on any of these attributes, (iii) zero-shot transfer to a separate dataset of out-of-distribution abstract social interactions, and (iv) shift spatial attention from scene context to socially informative regions (faces, gaze, and interacting bodies). A matched language-distillation control fails to reproduce these gains, ruling out caption transfer as the mechanism. Our results show how a modest amount of human behavioral data can steer video models toward human-like social visual understanding.

Video multimodal large language models (MLLMs) keep climbing video question answering benchmarks, yet shuffling the frames, masking the segment that supports the answer, or occluding the target object barely changes their predictions. The accuracy rests on appearance and language priors, not on the temporal evidence the question asks for. We trace this to the unit of post-training: rewards are computed on a single response to the original clip, so the model is never asked to behave consistently across views. We propose Behavior Pack Optimization (BPO), which replaces the single response with a behavior pack of outputs across counterfactual views chosen by question type, scored jointly. The pack reward asks for stability when the intervention is irrelevant, sensitivity when key evidence is removed, and abstention when no evidence remains. To keep this objective stable at small pack sizes, BPO uses an anchor-relative advantage: the response on the original view serves as a per-prompt reference instead of a group mean over mixed views. On TempCompass, MVBench, and NExT-QA, BPO improves the macro accuracy of Qwen2.5-VL-7B-Instruct by 4.7 pp, the temporal-hard subset by 7.8 pp, and abstention F1 by 20.0 pp over a budget-matched vanilla GRPO baseline from the same SFT checkpoint. The gains transfer to Video-MME, LongVideoBench, and to LLaVA-Video-7B; ablations confirm they follow the view sets, not the rollout count. We hope this pack-level perspective offers a useful starting point for the video MLLM and multimodal post-training community as the field moves toward evidence-grounded video reasoning.

Children's earliest years are a period of intense social learning, during which meaning is built through gaze, gesture, and physical contact. Yet, this critical aspect of social interaction remains under-evaluated in multimodal video understanding, as existing benchmarks prioritize the conversation-driven interactions of adults. We address this gap with NEST ($\textbf{N}$aturalistic $\textbf{E}$arly-childhood $\textbf{S}$ocial in$\textbf{T}$eractions), the first child-centric benchmark for nonverbal social reasoning. NEST comprises 1,208 manually annotated clips ($\sim$12s) curated for diversity across naturalistic settings, cultural contexts, and developmental stages. Beyond a lack of data, targeted model improvement in this domain is hindered by coarse evaluation schemas that conflate basic perception with higher-order reasoning. To resolve this, NEST introduces a compositional schema that builds atomic interaction cues into progressive levels of social reasoning: contextual, behavioral, interpersonal, and open-ended descriptions. Extensive analysis of state-of-the-art Vision-Language Models (VLMs) reveals a stark performance collapse: despite strong contextual recognition, models exhibit substantial gaps in decoding precise behavioral and interpersonal cues. We trace these failures to specific biases, including poor consequence grounding and temporal sensitivity. Finally, we ground NEST in real-world applications by evaluating VLMs using domain adaptation strategies. Ultimately, NEST provides a rigorous testbed for advancing socially grounded artificial intelligence in early childhood. The dataset and code will be publicly released.

Video Large Language Models (VideoLLMs) receive frames in sequential order and interpret how visual content evolves along the temporal axis, yet temporal reasoning remains a persistent weakness across architectures. Reversing the frame order of a video, a transformation that should invert temporal answers, often leaves the final prediction unchanged. We investigate where this failure originates by defining the temporal divergence vector $\tau_l$, the layer-wise representational difference induced by reversing temporal order. Tracking its magnitude across layers reveals a consistent temporal divergence profile where the divergence peaks at intermediate layers and progressively diminishes toward the output. We confirm this peak is specific to temporal reasoning and functionally critical for predictions, establishing that VideoLLMs acquire temporal information at intermediate layers but fail to maintain it to the output. This progressive fading motivates our method, Temporal Activation Injection (TAI), which extracts $\tau_l$ at the peak of the profile for each input and reinjects it into subsequent layers following the measured decay. TAI requires no training and consistently improves temporal reasoning across three VideoLLMs and four benchmarks with negligible impact on non-temporal tasks.

Current open-source diffusion models struggle to generate stable and synchronized audio-visual content, particularly in scenarios demanding complex semantic reasoning. The root cause is that existing methods rely on coarse text embeddings from off-the-shelf encoders to guide audio-video denoising, which discards fine-grained semantics and, critically, lacks a shared long-horizon plan, leading to uncoordinated denoising trajectories and fragile cross-modal alignment. We propose Baton, the first framework that introduces explicit semantic planning into joint video-audio generation. Our key insight is that complementing coarse text guidance with semantically rich, modality-aware planned tokens, jointly reasoned and mutually aligned before denoising, can simultaneously restore fine-grained semantic detail and establish a shared blueprint that coordinates both audio and video denoising trajectories. Concretely, Baton first introduces the VA-Planner, a multimodal language model equipped with dual semantic alignment towers, where learnable queries cross-attend to both video and audio features to produce a pair of semantically aligned video and audio planning tokens as keyframe-level blueprints. These planning tokens are injected into the diffusion backbone via cross-attention layers, providing temporally grounded guidance complementary to coarse text embeddings. Since planning tokens do not share one-to-one spatial-temporal correspondence with diffusion latents, we further propose Relative Semantic RoPE, a relative positional encoding that maps planning tokens and latents into a shared spatial-temporal coordinate frame, enabling each latent to accurately attend to its positionally corresponding semantic cues. Experiments on benchmarks show the effectiveness of Baton both qualitatively and quantitatively.

Transductive adaptation improves vision–language models at test time by refining predictions on an unlabeled batch, but current methods face a key trade-off. Aggressive approaches exploit batch structure but can drift when batches are sparse, have prior shift, or cover little of the label space. Conservative methods better preserve CLIP text semantics, but fixed semantic references can under-adapt even when the batch provides strong corrective evidence. We propose **JPTA** (Joint Prior and Transport Adaptation), a prior-aware framework that casts transductive VLM adaptation as batch-conditioned semantic reference estimation. Instead of treating CLIP text prototypes as fixed priors or freely replaceable, JPTA views them as semantic references that shift toward image-side batch structure only under calibrated evidence. It estimates batch priors and soft image-side prototypes to form transported semantic references whose impact is scaled by batch reliability. Across standard benchmarks, low effective-class regimes, all-class evaluation, and online streams, JPTA consistently outperforms TransCLIP and StatA, with the largest gains on sparse and prior-shifted batches. Anchor-drift, active-support, and absent-class-mass diagnostics show that JPTA fixes effective-class mismatch without unconstrained transductive drift, indicating that robust transductive VLM adaptation should recalibrate semantic references using reliable batch evidence rather than choose between unrestricted transduction and fixed prototypes.

Long-tailed recognition is commonly formulated as a problem of imbalanced supervision, where rare classes suffer from insufficient training examples. In this work, we study a complementary source of difficulty: under severe scarcity, tail classes may be forced into fine-grained discrimination before the model has acquired sufficiently reliable coarse semantic structure. We refer to this phenomenon as granularity mismatch under scarcity. To address it, we propose BasicLT, a two-stage framework that augments an underlying fine-grained recognition model with basic-level abstraction and commitment-guided selective differentiation. In Stage 1, BasicLT learns an explicit basic-level branch and aligns it with the family-level distribution induced by the fine-grained predictor, encouraging stable coarse semantic organization before additional fine-grained refinement is introduced. In Stage 2, a frozen Stage 1 teacher provides stable family-level commitment signals, and a family-conditioned residual refinement module is trained only on committed medium-shot and few-shot samples. At inference time, the residual correction is activated conservatively according to student-side commitment, so that fine-grained refinement acts as a conditional within-family correction rather than an unconditional auxiliary classifier. We evaluate BasicLT on CIFAR-100-LT, CIFAR-10-LT, ImageNet-LT, iNaturalist 2018, and Places-LT. Across these benchmarks, BasicLT achieves competitive performance against strong long-tailed baselines, with the most consistent gains appearing in medium-shot and few-shot regimes. Ablations and analyses further show that both basic-level abstraction and commitment-guided selective refinement are important, supporting the view that controlling the semantic granularity of discrimination can complement conventional rebalancing strategies for long-tailed learning. Code is available at Supplement.

Emotions are expressed on a continuum and are naturally described in the valence-arousal (VA) space. Existing VA-based image editors are typically training-based and domain-specific, limiting them to VA regions seen during training and making them brittle across datasets and domains. Meanwhile, despite producing high-quality edits, state-of-the-art foundation image editors are not continuous affective controllers: non-linear emotion representations in text encoders makes prompt interpolation poorly calibrated, so accurate and continuous VA control remains unreliable. To fill this gap, we introduce Barycentric Guidance, a training-free framework that turns pretrained foundation image editors into continuous affective controllers over VA, requiring no finetuning, extra data, or model changes. Our method maps any target VA point to simplex-based guidance weights, enabling VA controllability in three aspects: (i) accurate targeting of VA states, (ii) broad coverage across the VA plane, and (iii) continuous control. Across three face datasets, we improve VA target accuracy by 25–29\%, expand VA-plane coverage by 28–50\%, increase expression diversity by 29–49\%, over the strongest baseline. Unlike prior domain-specific methods, our training-free approach exploits the foundation model’s inherent versatility, enabling generalization beyond human faces to animals, artworks, and complex scenes within a single framework. Code will be made publicly available.

With the rapid adoption of large language models (LLMs) and parameter-efficient fine-tuning (PEFT) methods, the risk of backdoor attacks has become more severe. Existing backdoor purification methods typically rely on at least one of the strong assumptions, such as prior knowledge of triggers, access to clean references, or aggressive retraining, and they often lack comprehensive evaluations. These constraints substantially limit their practical applicability. To overcome these challenges, our work proposes purifying LoRA-tuned LLMs without these assumptions and even without post-hoc retraining of the suspect parameters. Our objective is to significantly reduce the attack success rates (ASR) while preserving both (i) the base model’s general capabilities and (ii) the new downstream skills learned through the adapter. Through a series of ablation studies, we progressively scale our approach from a single layer in a text classification setting to a full-parameter LLM in the generative task. Through careful data curation and feature approximation, we extract high-fidelity backdoor directions and, for each layer or head, construct orthogonal null spaces in both the input and output channels, onto which the LoRA updates are projected. Empirically, our null-space projection method reduces the ASR from nearly 100% to less than 10%, while preserving the base model’s benign performance as well as the adapter’s abilities learned during downstream task adaptation.

How would we know whether a machine truly understands the world? We argue that one key capability is \textit{observational theory learning}: acquiring reusable theories from pure observations and applying them to new situations. Current visual reasoning benchmarks focus primarily on the latter, presenting each task as a few-shot puzzle grouped by a common rule. We introduce BabyTheorist, a benchmark for controlled training and evaluation of observational theory learning. BabyTheorist generates continual streams of before-after visual observations from hidden compositional programs; during training, learners see only the resulting transitions, with no program labels, task identities, or support sets. To support theory learning and evaluation, BabyTheorist controls the program language, a curriculum over program complexity, recurring frequent subprograms, and a continual train-test protocol with level skipping. Seven representative baselines plateau well below ceiling, with the gap widening as the curriculum advances---evidence that BabyTheorist isolates a capability current program-learning approaches do not yet address.

Structured multiple-testing problems (gatekeeping trials, dose-finding, multi-tissue eQTL mapping, bundled-challenger A/B experiments) organize hypotheses into design-imposed blocks and demand strong family-wise error rate (FWER) control for confirmatory claims. Practitioners currently use objective-agnostic stepwise rules (Bonferroni, Holm, Hochberg, Hommel), closed-testing and graphical extensions, or hierarchical and resampling methods; none is power-optimal within the block-separable class these designs induce. We introduce **BOOST** (Block-Optimal Objective-driven Strong-FWER Testing), the power-optimal strong-FWER procedure for block size three, with three guarantees: (i) finite-sample strong-FWER validity at $O(K)$ cost (versus $O(K^2)$ for general closed testing) without independence assumptions, with a strict Šidák improvement under cross-block independence; (ii) power-optimal allocation across heterogeneous blocks via an equalized-marginal KKT condition, solvable by bisection in $O(B\log(1/\varepsilon))$; and (iii) a sample-split plug-in variant for unknown alternative density $g$, attaining $\alpha$-control up to $O(B_{\mathcal{T}} \, \mathbb{E}\Vert g - \hat{g}\Vert_{\infty})$ inflation with per-hypothesis power deficit *independent of* $B_{\mathcal T}$. Simulations across independent, equicorrelated, sparse, and mis-specified regimes show 1.4–1.7× power gains over the strongest existing baseline at calibrated FWER. On two published datasets (BLUEPRINT cross-lineage cis-eQTL and Upworthy bundled-challenger A/B experiments), BOOST certifies an order of magnitude more full-block discoveries than existing baselines at controlled FWER.

Federated vision–language models (FedVLMs) represent one of the emerging frontiers of machine learning, aiming to integrate federated learning within the fine-tuning pipelines of vision–language models (VLMs). In this work, we introduce BLEND, a new framework designed to jointly enhance the personalization and generalization capabilities of FedVLMs. BLEND proposes a selective VLM parameter fine-tuning and aggregation strategy that consists of (i) global vision and text adapters shared across clients, and (ii) a personalized vision projection adapter tailored to each client. BLEND further introduces a novel loss function that blends the outputs of the global and personalized adapters to achieve personalization, while simultaneously promoting generalization through a principled anchor design implemented via the projection heads of the zero-shot VLM. Extensive experiments demonstrate that {BLEND} consistently outperforms state-of-the-art baselines, highlighting its ability to effectively balance personalization and generalization across diverse system settings. (Code is available at: https://github.com/blendfedvlm/BLEND.git)