NeurIPS 2026 论文列表

Low-bit storage of optimizer states can substantially reduce the memory footprint of large-scale training. In practice, however, these states are not quantized once: at every training step, they are dequantized, updated, requantized, and written back. This iterative quantization pipeline can destabilize training. We identify a failure mode behind this instability: for states updated via exponential moving averages (EMAs), quantization errors are recursively fed into subsequent updates. As a result, biased errors accumulate over time while high-variance errors are amplified, particularly when the EMA decay factor is close to 1. To address this error accumulation, we propose **BReD **(**Block **Re**play **D**ithering), a method that reduces rounding bias and controls rounding variance during quantization. BReD adapts classical subtractive dithering to optimizer-state quantization by combining deterministic seed replay with block-wise shared dither values, avoiding auxiliary random-tensor storage and per-element dither values generation. Across pretraining (five optimizers at 120M; AdamW and Muon at 1.1B and 3.4B model) and supervised fine-tuning (AdamW and Muon at 7B model), 4-bit BReD closely matches training with full-precision optimizer states, with PPL shifts $\leq0.5$ in pretraining and $\leq0.2$ in fine-tuning and average downstream performance changes within 0.7 points. Moreover, 3-bit BReD preserves stable convergence in the evaluated settings, offering a more aggressive alternative.

Stochastic bilevel optimization (SBO) has become a standard framework for hyperparameter learning, data reweighting, representation learning, and data-mixture optimization in deep learning. Existing exact single-loop SBO methods and memory-efficient surrogate SBO methods either create severe memory pressure for large lower-level neural networks or lack competitive convergence guarantees under standard assumptions. In this paper, we propose BROS, a memory-efficient single-loop SBO method with the same convergence rate order as exact single-loop SBO methods. BROS performs lower and auxiliary updates in randomized subspaces with a Rademacher bi-probe correction that recovers an unbiased Hessian-action estimator. We prove that BROS preserves the $\mathcal O(\varepsilon^{-2})$ sample complexity of MA-SOBA for finding an $\varepsilon$-stationary point under only standard assumptions. Experiments on hyper-data cleaning, data-mixture learning, hyper-representation learning, and ViT sample reweighting show that BROS reduces peak memory by up to 44.9% while closely matching full-space baseline performance.

Parameter-efficient fine-tuning of quantized large language models is dominated by QLoRA, which combines 4-bit weight quantization with FP16 low-rank adapters. We show the FP16 adapter precision is unnecessary: a single binary bit per adapter parameter, combined with a learned per-layer scale $s$ and a Scaled Straight-Through Estimator, matches or exceeds QLoRA across a broad range of benchmarks while reducing adapter storage by $14$--$16\times$. We introduce two variants--NormScale, which clips STE gradients to the unit ball, and LearnThresh, which makes the binarization threshold learnable---unified under a framework we call BQ-LoRA. Our main theoretical contribution is a tight characterization of the BQ-LoRA hypothesis class: it has bounded entrywise norm, a high-probability spectral norm bound that is $\sqrt{r}$ times sharper than the worst case, a generically full-rank update, and consequently a Rademacher complexity that yields an explicit $\tilde{O}(s_{\max}\sqrt{d_{\text{in}} d_{\text{out}}/n})$ generalization bound. None of these properties hold a priori for FP16 LoRA. On Qwen2.5 (1.5B/7B/72B) and LLaMA-2 (7B/70B), BQ-LoRA matches or beats QLoRA and LoftQ at $\sim 1/16$ the adapter footprint; the predicted regularization signature appears empirically in dataset ablations and training-loss curves. We extend Punica's SGMV kernel with a binary inner-product primitive (BQ-SGMV) and prove a cache phase-transition result: shrinking adapters $16\times$ enlarges the HBM cache from $2{,}500$ to $39{,}000$ adapters, eliminating misses under skewed workloads.

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.

High-dimensional Bayesian optimisation (HDBO) is an increasingly crowded field, with new methods proposed at every major conference, making it ever harder to answer a deceptively simple question: "Which method is currently state-of-the-art?" In this paper, we question the efficacy of benchmarking practices in the field, and find them to be systematically ineffective. Specifically, multiple published works fail to benchmark against the strongest baselines available at the time of writing, or even include incorrect implementations of key methods. As a remedy, we introduce BO-Arena, an actively maintained and extensible package providing canonical implementations of state-of-the-art algorithms, designed to streamline building new methods and rigorously benchmark against existing approaches. We use the package to ask what currently drives performance in HDBO, and find that the answer is even simpler than previously thought. Following these simple principles, we propose a new algorithm which achieves state-of-the-art performance on problems with large numbers of observations, outperforming considerably more complex recent methods. Finally, we question whether there is currently sufficient evidence to suggest that a simple Gaussian process baseline is outperformed by any more complex methods for HDBO.

The deployment of Large Language Models (LLMs) with extended context windows is fundamentally constrained by the quadratic computational and memory costs of the self-attention mechanism. While acceleration techniques like sparse attention and quantization offer potential relief, they currently face a critical dilemma: uniform compression strategies fail to capture the non-uniform distribution of information importance, degrading performance on long-range dependencies; conversely, fine-grained, token-level adaptation often introduces irregular memory access patterns that negate theoretical efficiency gains on modern GPUs. To resolve this tension, we introduce BMAttn (Block-Aligned Mixed-Precision Attention), a framework that unifies importance-aware precision allocation with hardware-friendly execution. BMAttn partitions attention maps into block-aligned high-precision, low-precision, and sparse regions, using a novel affine windowing mechanism to dynamically adjust boundaries based on sequence length. We further propose a saliency-weighted calibration method and a layer-adaptive regularizer that adaptively aligns precision targets with perceptual importance and layer sensitivity. Extensive experiments demonstrate that BMAttn achieves a \textbf{3.3x} speedup on long-context tasks with negligible accuracy loss, and up to \textbf{5x} speedup with minimal degradation, effectively bridging the gap between algorithmic adaptivity and hardware efficiency.

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)

Backpropagation imposes fundamental constraints on training neural networks. It requires the retention of all forward activations until the corresponding backward pass completes, thereby preventing pipelined execution across layers. This work introduces Backpropagation-Free Local Antithetic Node Perturbation (BLANP), a fully local training framework that eliminates gradient propagation across layer boundaries while requiring no negative samples. Each layer updates its parameters from locally available signals through a zeroth-order antithetic perturbation estimator. To stabilize learning, the estimator is evaluated against a frozen exponential moving average critic, decoupling backbone updates from the concurrently evolving local classifiers. The antithetic perturbation formulation cancels even-order bias terms in the gradient estimate, yielding a first-order accurate update from two forward passes alone. A companion variant, BLANP-Exact, replaces the perturbation estimator with exact yet local gradients under a strict stop-gradient constraint, preserving a memory footprint that does not grow with network depth while recovering gradient fidelity. Experiments on MNIST, Fashion-MNIST, and CIFAR-10 across MLP and convolutional architectures show that BLANP matches backpropagation on simple benchmarks. On the more challenging CIFAR-10, BLANP-Exact surpasses the best prior local learning method by 2.40% with a simpler architecture, and trails an identical backpropagation baseline by only 3.90%. The code is available at https://github.com/anonymous/BLANP.

As Large Language Model (LLM) datasets scale to trillions of tokens, data selection has emerged as a critical frontier to filter out uninformative noise and construct adaptive learning trajectories. Beyond static heuristic filtering, advanced data selection methods for LLM training largely follow two paradigms, each with fundamental limitations. Influence-based methods provide principled bi-level objectives but require intractable inverse-Hessian computations, while excess-loss methods are computationally efficient but rely on a static reference model that becomes misaligned with the evolving proxy model during training. We propose BLADE (Bi-Level Adaptive Data sElection), a Hessian-free framework for data selection. BLADE reformulates the bi-level optimization problem underlying influence-based methods as a penalized single-level objective via Lagrange multipliers, avoiding inverse-Hessian computation while revealing a principled connection to excess-loss based data selection. The resulting objective recovers an excess-loss form but replaces the static reference model with a dynamic one that stays synchronized with training. Theoretically, we prove that this penalized formulation guarantees first-order convergence. For efficient online batch selection, we instantiate BLADE as a memoryless randomized block-coordinate Frank-Wolfe algorithm. Extensive experiments show that BLADE consistently outperforms state-of-the-art data selection baselines, providing a practical recipe for LLM training.

Modern data-centric applications increasingly require language agents capable of operating over live, stateful database environments, where successful problem resolution depends not merely on initial SQL generation, but on reasoning about evolving database states, interacting with execution feedback, and repairing state-mutating operations. While agentic reinforcement learning (RL) has emerged as a promising paradigm for training small language models (SLMs), existing frameworks are predominantly tool-centric and struggle with data-centric environments. Specifically, applying tool-centric frameworks to stateful tasks like SQL debugging introduces severe spatial inefficiencies since proper environment isolation typically requires a separate database container per rollout. Naively reusing databases without tracking evolving states across multi-turn interactions causes trajectory contamination and cross-trajectory interference, which severely destabilizes policy updates. To overcome these bottlenecks, we introduce **BIRD-RL**, a novel four-stage agentic RL curriculum that alternately enhances foundational agent reasoning and trajectory-adaptive on-policy exploration to circumvent the instability inherently associated with direct end-to-end learning. Crucially, to support this curriculum, BIRD-RL is driven by a **Trajectory-scoped Persistent Execution Infrastructure**, which enables safe database replica reuse while preserving trajectory isolation. This design improves spatial RL efficiency by **12.8x**, successfully enabling scalable agentic RL for complex data-centric tasks without needs of Kubernettes. Empirical evaluations on the BIRD-CRITIC-SQLite benchmark demonstrate that our 7B and 14B models achieve Success Rates of **44.40%** and **48.00%**, respectively, performing on par with or exceeding strong proprietary model-based agents such as Claude-Opus-4.6 Agent (48.20%). Furthermore, BIRD-RL successfully trains unified SLMs capable of both advanced SQL debugging and generation, highlighting its broader potential for multi-objective optimization across heterogeneous, real-world data-centric tasks.

Given the surge of harmful AI-generated imagery online, reliably distinguishing authentic images from generated ones has become an urgent research topic. While many proposed detection methods perform well under controlled settings, they often collapse when tested on real-world data. A potential root cause are subtle biases in the detectors' training data. As a result, detectors may rely on spurious correlations instead of learning true forensic artifacts. While a recent line of work has identified the problem, there is not yet an established protocol to evaluate how biased a detector actually is. In this work, we therefore take a step back: First, we discuss what it means for a detector to be biased, and how this differs from a lack of robustness. Second, we propose BIAS-ID, a transparent framework for analyzing and quantifying the presence of transformation biases in AI-generated image detectors. We validate our framework by performing an evaluation of six detectors across two datasets, revealing that several state-of-the-art detection methods are strongly affected by biases. Our results highlight the importance of bias-aware evaluation for developing reliable AI-generated image detectors.

Time-resolved stellar spectroscopy is a primary observational window onto mass-loss, disc formation and rotational physics, phenomena that single-epoch surveys cannot capture. Be stars are rapidly rotating B-type stars that episodically grow and lose a circumstellar gas disc, which imprints a variable emission profile on the hydrogen H-alpha Balmer line (6562.8 Angstrom) on timescales from days to decades. Decoding these spectral variations is both a key step in understanding stellar evolution and an open challenge for machine learning on irregularly sampled scientific time series. Yet no benchmark exists for these spectra: professional surveys deliver a single epoch per star, and the amateur-driven BeSS database, although it hosts hundreds of thousands of multi-epoch spectra, has never been curated for machine learning. We close this gap with BESS-Bench: 339,115 optical spectra of 1,468 Be stars over 35 years, all expert-reviewed, from amateurs (81.7%) and professionals (17.7%), with a scored H-alpha benchmark slice of 26,937 spectra and three tasks under a unified evaluation protocol. SpecProbe measures how well frozen embeddings recover six scalar line features (width, peak separation, line-core depth, asymmetry, equivalent width, peak intensity). LineTransfer tests cross-line generalisation (H-beta to H-alpha). EWForecast predicts the next-epoch equivalent width of H-alpha (a disc-mass proxy) from a star's recent spectroscopic history. We release a compact masked auto-encoder baseline (BeMAE) and benchmark it against PCA and two zero-shot time-series foundation models (Chronos-Bolt and TimesFM-2.0). The ranking is sharply task-dependent: BeMAE recovers shape features with an R2 that is 4.24x that of PCA(10) (bootstrap CI95 [4.12, 4.33]), yet a PCA+ridge pipeline lowers EW forecasting MAE by 5.8% over persistence, beating both foundation models, and calling as such for further development of AI models in this context. Dataset, weights, frozen splits and evaluation pipeline are released under CC-BY-4.0, so the scientific community can adopt BESS-Bench as a shared testbed and contribute new representations and models to advance our understanding of stellar variability.

LLM supervision systems, namely input/output moderation filters and jailbreak detectors, are the primary safeguard against misuse in deployed AI applications, yet existing benchmarks are often vendor-biased, omit cost and latency, and rarely compare specialized guardrails against repurposed generalist LLMs. We present BELLS-O (Benchmark for the Evaluation of LLM Supervision Systems -- Operational), the first independent operational benchmark of LLM supervision systems. BELLS-O evaluates 28 systems from 17 providers: every major specialized guardrail (e.g., LlamaGuard-4, ShieldGemma-2, Lakera Guard) and frontier generalists repurposed as supervisors (e.g., GPT-5.4, Claude Sonnet 4.6, Grok-4.1), jointly on detection rate, false-positive rate, latency, and monetary cost. We cover input/output moderation across 11 harm categories and jailbreak detection across 13 attack techniques, using in-house datasets built from handcrafted prompts, expert-curated samples, and quality-controlled synthetic generation. To prevent latent generator-specific signals in synthetic data, every generated sample is run through a paraphrasing step that suppresses these fingerprints. Mapping the Pareto frontier reveals use-case-dependent tradeoffs. On content moderation, specialized supervisors are operationally dominant: top systems match frontier LLMs on detection ($\approx$95\% vs.\ 94\%) at comparably low false-positive rates ($\leq$2\%), while running 5--10$\times$ faster and ${\sim}$10$\times$ cheaper. On jailbreak detection, the tradeoff shifts: frontier LLMs achieve higher detection and lower false-positive rates but at 10--50$\times$ higher cost and 5--10$\times$ higher latency. We release the benchmark, framework, leaderboard, and datasets as the first vendor-neutral basis for selecting safeguards under real deployment constraints.

Constrained preference-conditioned MORL typically assumes full task observability, treating preferences and safety budgets as stationary task signals. In real-world deployment, however, these signals are often unreliable proxies: intent may drift, safety tolerances may shift with context, and exposed descriptors may be noisy, delayed, or incomplete. We therefore formulate constrained preference-conditioned MORL as a partially observed task-specification problem, where the agent must navigate a fundamental conflict between unreliable external signals and latent interaction dynamics. We propose \textbf{BECON}, a belief-conditioned framework for constrained MORL with drifting preferences and budgets, which infers latent task context from recent histories to estimate preferences and safety budgets. These estimates are fused with the exposed task signal via uncertainty-dependent gates, enabling the policy to defer to observations under high epistemic uncertainty while correcting them when history is informative. The fused context jointly conditions preference optimization and context-adaptive constraint enforcement. Experiments on diverse multi-objective control tasks demonstrate that BECON consistently improves over state-of-the-art MORL baselines, achieving stronger robustness and constraint satisfaction under task drift.

Simulating student learning behaviors in open-ended problem-solving environments holds potential for education research, from training adaptive tutoring systems to stress-testing pedagogical interventions. However, collecting authentic data is challenging due to privacy concerns and the high cost of longitudinal studies. While Large Language Models (LLMs) offer a promising path to student simulation, they suffer from competency bias, optimizing for efficient correctness rather than the erratic, iterative struggle characteristic of novice learners. We present BEAGLE, a neuro-symbolic framework that addresses this bias by incorporating Self-Regulated Learning (SRL) theory into a novel architecture. BEAGLE integrates three key technical innovations: (1) a semi-Markov model that governs the timing and transitions of cognitive behaviors and metacognitive behaviors; (2) Bayesian Knowledge Tracing with explicit flaw injection to enforce realistic knowledge gaps and "unknown unknowns"; and (3) a decoupled agent design that separates high-level strategy use from code generation actions to prevent the model from silently correcting its own intentional errors. In evaluations on Python programming tasks, BEAGLE significantly outperforms state-of-the-art baselines in reproducing authentic trajectories. In a human Turing test, participants could not reliably tell BEAGLE traces apart from real student data: classification accuracy was statistically equivalent to chance (52.8\%, $d'=0.15$, $N=71$).

Large language models have achieved remarkable capabilities by scaling model capacity and training data, yet many practical deployments still rely on smaller models with limited resources whose capabilities lag behind their larger counterparts trained with richer resources. This gap calls for efficient knowledge transfer from source models with richer resources to compact target models. While model merging provides an effective mechanism, most existing methods are based on the assumption that the source and target models are architecturally compatible, making them inapplicable to heterogeneous source and target pairs. Although a recent method based on optimal transport extends model merging to settings with different architectures, it remains limited by linear correspondence modeling, iterative transport optimization, and reliance on supervised adaptation after fusion for further performance gains. To address these limitations, we propose BDC-Merge, a framework for model merging across different architectures based on Brownian Distance Correlation (BDCorr). BDC-Merge uses a small calibration set to estimate dependencies between heterogeneous activations at the feature level and the layer level, and directly lifts these dependencies from activation space into fusion operators in weight space, enabling effective parameter fusion across different architectures without any gradient based optimization or training after fusion. Extensive experiments across four low resource language and two specialized knowledge benchmarks show that BDC-Merge consistently outperforms the state of the art baseline for model merging across different architectures and largely preserves the target model’s general capabilities.

Formulating an optimization problem strongly affects the quality of the final solution, yet good formulations usually require substantial expertise. Recent studies have therefore examined how to automatically derive optimization problems from natural-language descriptions, but existing benchmarks focus on settings where objectives and constraints can be written explicitly as mathematical expressions. Many practically important problems, including hyperparameter optimization in machine learning, are naturally treated as black-box optimization (BBO) problems, in which only objective values are observable, and the functional form is unavailable. In BBO, the search space design, a part of the problem formulation, and the selection of the optimization algorithm are crucial for problem-solving. Automating these processes with large language models (LLMs) is a significant challenge. This paper introduces Black-Box Optimization Word Problems (BBOWP), a novel problem setting in which a system must infer both a search space and an optimization algorithm from a natural-language description of a black-box optimization task. To support research on this setting, we establish the BBOWP Benchmark Suite (BBOWP-Bench), a dataset and evaluation framework for BBOWP. Each instance combines a natural-language problem description, an executable evaluation environment, and a human-designed baseline formulation, allowing evaluation of both search-space design and algorithm selection. The benchmark covers 23 instances from four different application domains and supports reproducible assessment through actual black-box optimization runs. Using this benchmark, we provide the first evaluation of LLMs and show that current LLMs are capable of selecting suitable algorithms based on the given evaluation budget. However, they sometimes struggle with search space design, particularly in identifying important variables and balancing their ranges when the problem description is less informative or the search space is highly problem-specific.

Feed-forward transformer models have driven remarkable progress in 3D vision, yet their quadratic complexity renders them impractical for long video sequences. Streaming 3D reconstruction addresses this bottleneck by processing frames sequentially with constant memory. However, existing recurrent architectures inevitably suffer from progressive degradation over long sequences, as they fail to account for the intrinsic reliability of observations and the accumulated confidence of the historical state, leading to severe error accumulation. To address these issues, we introduce BAT3R, a training-free Bayesian adaptation framework comprising two synergistic modules: State Innovation Gating (SIG) and Bayesian-guided Adaptive State Updating (BASU). SIG acts as a distributional regularizer that anchors state evolution to its geometric initialization, effectively arresting representational drift during long-term inference. Building on this, BASU reformulates state update as a recursive Bayesian inference process within a Kalman filtering framework. By jointly modeling the temporal reliability of observations via information-theoretic gain and spatial relevance via cross-attention maps, BASU achieves an asymmetric spatio-temporal state fusion: well-consolidated historical regions are protected from redundant updates, while genuinely novel scene content is aggressively incorporated. Extensive experiments on standard benchmarks (7-Scenes, TUM, ScanNet, KITTI, etc.) demonstrate that BAT3R consistently outperforms state-of-the-art online 3D reconstruction methods, with the most significant gains observed on long sequences and complex scenes.

Few-shot medical image segmentation has shown great potential in reducing annotation costs for clinical applications. Recently, many few-shot methods have explored the Segment Anything Model (SAM) for training-free medical image segmentation via prompt engineering. However, existing approaches mainly focus on locating positive prompts from support images while overlooking informative background cues, making them prone to over-segmentation in anatomically similar regions. Moreover, simply introducing negative prompts cannot effectively suppress boundary leakage, and may even interfere with SAM’s mask decoding process, resulting in worse performance than using positive prompts alone. To address this limitation, we propose a training-free support-query framework, termed Boundary-Aware Prompt Mining, which introduces boundary-aware negative prompting for SAM-based few-shot medical image segmentation. Specifically, we introduce a Prototype-guided Positive Prompt strategy, which adopts a multi-center prompting mechanism to construct multiple foreground prototypes, enabling comprehensive spatial coverage in query images. Furthermore, we propose a Boundary-band Ambiguity Filtering strategy that identifies a boundary-adjacent background band and progressively removes ambiguous pixels and unreliable clusters highly similar to the foreground, enabling the selection of reliable negative prompts for suppressing false positives near foreground boundaries. The generated positive and negative prompts are jointly fed into SAM to produce refined segmentation results without additional training. Extensive experiments on Abd-MRI and Abd-CT datasets demonstrate that our method consistently outperforms existing approaches, highlighting its effectiveness and robustness under limited annotation settings. Code will be released upon acceptance.