NeurIPS 2026 论文列表

Machine learning models—including large language models (LLMs)—are often said to exhibit *monoculture*, where outputs agree strikingly often. However, we present two experiments that substantially disagree with prior findings of monoculture. In particular, we find that monoculture virtually disappears in one dataset when we include item difficulty compared to previous works that do not. Informed by these findings, we theoretically formalize what it means to measure monoculture. We show that monoculture is inherently subjective, relying on two key decisions: 1) the baseline null model for what "independence" should look like; and 2) the population of models and items under consideration. We conclude with concrete guidelines for future evaluators to ensure the robustness of their results.

Class-incremental learning systems trained with $\ell_2$-regularized cross-entropy and within-task softmax exhibit a structural bias on cross-task discriminators that no amount of data or regularization tuning can remove. We characterize this bias in closed form: at the population optimum, the cross-task discriminator under sequential within-task training differs from the joint-optimal by exactly $\Sigma^{-1}(\bar\mu^{(j)} - \bar\mu^{(i)})$, an offset arising from the per-task gauge fix imposed by within-task softmax, identical for all $C^2$ class pairs in a task block---this invariance, not the closed form alone, is what makes the diagnosis falsifiable and the correction one-line. Under a separation condition ($\kappa > 1$, cross-class distances exceeding inter-task distances) we prove a margin-based bound on task confusion at rate $\exp(-(\kappa - 1)^2 \gamma_{ij}/8)$. By explicit construction we show this bound cannot be uniform: when separation fails, cross-task misclassification remains $\Omega(1)$ regardless of inter-task margin, and on configurations with extreme within-task class-norm asymmetry it converges to $1$. We propose \emph{post-hoc de-centering}, an evaluation-time correction that adds the per-task offset $\Sigma^{-1}\bar\mu^{(t)}$ to each weight and a corresponding scalar to each threshold; it recovers the Bayes-optimal cross-task discriminator at the population exactly and yields a bound on task confusion that does not require the separation condition. The same operation appears in shared-covariance LDA-style heads (SLDA, FeCAM, NCM); our contribution is the gauge-theoretic derivation identifying it as the unique per-task affine correction to the structural bias of within-task cross-entropy training, the falsifiable empirical diagnostic across five published CIL methods, and the finite-sample analysis. Experiments on CIFAR-100, ImageNet-100, ImageNet-1K, ImageNet-R, and DomainNet across five frozen backbones show post-hoc de-centering recovers $82$--$94\%$ of the joint--sequential gap (mean $90.5\%$).

Transformers use the same forward computation stream to both predict the next token and store useful state for future token predictions. We formulate the \emph{state-prediction separation hypothesis}: disentangling the two roles yields better language modeling performance. We design a Transformer variant that uses separate computation streams to separate the two functions, and conduct pretraining experiments across various scales. Our experiments show that state-prediction separation consistently offers better data-- and compute--efficiency, improving validation loss and outperforming standard Transformers by 2--3 percentage points on average on downstream tasks. We also conduct extensive empirical analysis that rules out potential confounders and demonstrates the fundamental difference in the gradients our design entails.

We study the stability of data exchange among competing firms. Unlike tangible goods, data is non-rival: sharing it does not deprive the giver of its use. On one hand, competing firms-- often operating as machine-learning–driven predictors that collect observations from a shared environment to solve similar tasks-- can improve their predictive accuracies from pooling data. On the other hand, they compete for market share, which may depend on their relative predictive performance. This raises the central question of this paper: which data exchanges are stable in such competitive environments? We formalize this via a model of $n$ firms exchanging fractions of their datasets, with predictive performance feeding into market competition. We adopt core stability as our solution concept, accounting for externalities via worst-case responses by non-participating firms. Our main results reveal a sharp contrast driven by demand structure: (i) in *fully rational* markets, where customers flock entirely to the top-performing firm, a core-stable exchange always exists for any monotone continuous prediction and utility functions; (ii) in *partially rational* markets with logit-style demand, core-stable exchanges may fail to exist, and even approximate cores can be empty. Our impossibility results suggest that internal profit-sharing within coalitions can restore stability in partially rational markets. We formalize this intuition by showing that when co-operating coalitions equally redistribute market-share revenues among members, a core-stable data exchange exists. Finally, we propose a natural exchange dynamic and demonstrate empirically that it converges to a core-stable data exchange in fully rational markets on simulated instances derived from real datasets.

The mechanism governing the training dynamics of Quantum Neural Networks (QNNs) remains under-explored. In classical Deep Neural Networks (DNNs), training is known dominated by "Spectral Bias,'' i.e. prioritizing learning low-frequency while struggling with high-frequency. QNNs exhibit similar spectral limitations when the target function possesses flat spectral amplitudes. However, in this work, considering target problems with different spectral amplitudes, we theoretically and empirically identify a distinct mechanism in QNNs, which we term *Spectral Amplitude Priority*. By analyzing the frequency-domain gradients and residual dynamics via the Quantum Neural Tangent Kernel (QNTK), we prove that QNN training is governed primarily by the magnitude of spectral components rather than their frequency indices. Consequently, QNNs can efficiently capture high-frequency functions—provided they have significant amplitude—thereby overcoming the inherent limitations of their classical counterparts. We validate this principle on both synthetic high-frequency functions and quantum-advantage tasks. These results show that QNNs notably outperform DNNs in high-frequency tasks, offering an explanation for QNNs' superior expressivity.

Pruning reduces the inference cost of large language models, but existing criteria primarily preserve large activations or reconstruct layer outputs. We argue that this overlooks a key computation performed by particularly sparsity-sensitive neurons in the MLP up and gate projections: separating similar inputs into dissimilar outputs. This suggests that effective pruning should preserve not only activations, but also pairwise output differences. We introduce a family of difference-informed pruning methods built upon this principle. Wisp is a first-order, update-free method that scores weights using input-difference norms, and Wisp+ refines this score neuronwise using the input pairs each neuron separates most strongly. Finally, Whisper is a second-order method that uses a lightly regularized difference Hessian as its reconstruction objective. Across the Llama 2 and 3.1 families, our second-order variant consistently improves language modeling performance over strong reconstruction-based baselines, while our update-free variants improve over activation-aware update-free baselines, with gains stronger in more constrained settings. The improvements over Wanda and SparseGPT extend to structured sparsity, downstream evaluations, and other model families, suggesting that preserving input differentiation is a broadly useful signal for post-training LLM sparsification.

Every neuron in a trained network outputs a number that can be separated into its sign (positive or negative) and its magnitude (absolute size). We call the set of all neuron signs the \textit{sign code}. This raises a key question: does classification depend more on the sign code or on the magnitudes? For overparameterized vision classifiers with sign-preserving activations, the answer is overwhelmingly the sign code. A $k$-nearest neighbor classifier operating only on this binary sign code achieves accuracy comparable to the full model across different architectures, activation functions, and datasets. Moreover, replacing all magnitudes in every layer with random noise while keeping the original signs barely changes residual network performance. In contrast, flipping just 20\% of the signs reduces accuracy to chance. Models generalize well when same-class inputs share similar sign codes. When this overlap is disrupted by a regularizer, training accuracy remains high, but test performance collapses. A margin decomposition helps explain these results: the sign-only contribution to each logit grows as $\sqrt{d}$ in network width, dominating the magnitude residual. The natural unit of analysis for these networks may not be through their continuous weight space but instead through their discrete sign codes.

Activation steering adds a vector to a language model's internal representations at inference time. It is a lightweight alternative to fine-tuning for behavioral control. The standard construction, _contrastive activation addition_ (CAA), is fragile, as on many inputs it shifts behavior in the wrong direction. To explain this fragility, in this paper we first study the geometry of the steering vector. Specifically, we introduce **steering Hessian**, _the matrix of second order derivative of the model's behavioral loss with respect to the steering vector_. It captures the output behavior response sensitivity of the model under small perturbations of the steering vector along each direction. Its top eigenvectors are _sharp directions_: tiny perturbations along them can cause large, erratic, input-dependent behavioral swings. On the other hand, bottom eigenvectors are _flat directions_: behavior is robust along them. We identify that removing the sharp component of a steering vector approximately preserves its direction and magnitude (cosine $> 0.974$, norm reduction $< 3\%$ for typical settings) while reducing the vector's directional curvature by up to 12$\times$ (median 4$\times$ across 36 conditions). We evaluate on three model families (Qwen2.5-7B, Llama-3.1-8B, Mistral-7B), with four behavioral tasks, and 36 token-level conditions. Steering along sharp directions reverses the intended behavior in every condition tested, with anti-steer rates of 53--100\%. Sharp directions account for only 1--5\% of the CAA vector's squared norm. However, they drive a disproportionate share of the gap between the steered and unsteered next-token distributions during autoregressive generation. We term this one-line correction as _sharp removal_; it outperforms standard CAA in open-ended generation across all 25 conditions tested. A jailbreaking experiment further shows that sharp directions act as _noise_ rather than signal: they disrupt coherent behavioral control regardless of steering polarity.

Convolutional neural networks trained on ImageNet are known to rely heavily on local high-frequency texture, an inductive bias that translates into fragile robustness against distribution shifts in real-world environments. Event cameras, in contrast, record only changes in scene brightness and are therefore well suited to capturing contour information; however, due to the absence of diagnostic benchmarks in the event domain, the inductive bias that event-camera data instills in vision models has remained unexplored. In this work, we use knowledge distillation from the event domain to the RGB domain so as to exploit the rich evaluation toolkit available in the RGB domain and systematically dissect this inductive bias. Our experiments show that distillation from the event domain induces, in the RGB domain, notable color invariance, shape bias, and robustness to high-frequency noise. We identify the underlying mechanism as the model suppressing its dependence on high-frequency texture while simultaneously acquiring a strong dependence on edge-based object shape. This hypothesis is supported by changes in how color and spatial information are processed at the early layers, together with a "spectral trade-off" in which robustness to the absence of high-frequency components coexists with vulnerability to contamination of the relied-upon frequency bands and to disruption of geometric structure. We further show that this inductive bias differs markedly from existing robustification methods and that it functions as a strong prior for diverse downstream tasks that demand shape-based reasoning, such as medical imaging. The code and experimental configurations for reproducing our experiments are submitted alongside this paper as supplementary material.

A central obstacle in nonlinear Bayesian filtering is representing the belief distribution. Moment-based filters address this by propagating polynomial moments and reconstructing a density from them. Recent work completes the predict-update loop via the maximum-entropy (MaxEnt) principle, but each step requires the partition function and its gradient, both $n$-dimensional integrals whose cost scales exponentially, restricting the demonstrated MaxEnt moment filtering to $n \le 4$. We avoid the partition function entirely by combining score matching with Stein's identity. In our setting, score matching reduces the density fit to a single linear solve whose coefficients are assembled directly from the propagated moments. The same parameters then drive Stein's identity to close the moment hierarchy during prediction and to recover posterior moments after each Bayesian update, keeping the full predict-update loop free of partition function evaluation. The resulting $\textbf{Score Kalman Filter}$ (SKF) reduces to the classical information-form Kalman filter as a special case and performs every step through linear algebra. On nonlinear coupled-oscillator networks, the SKF runs through $n=20$ and reports lower RMSE than the EKF, UKF, EnKF, and particle-filter baselines on the tested synthetic benchmarks.

LLM agents increasingly rely on external tools exposed via the Model Context Protocol (MCP), yet current benchmarks assume tools respond correctly and on time, leaving agent resilience to realistic tool failures untested. We introduce MCP-Injector, a drop-in, protocol-aware fault-injection middleware that interposes on the MCP tool-call path with controlled persistence (permanent, transient, intermittent) and seedable schedules. Its fault model is empirically calibrated by mining 57,240 issue and pull-request threads across 1,774 open-source MCP repositories, yielding a distribution over six failure modes grounded in observed ecosystem incidents. In a 1,350-cell experiment crossing two backbone sizes (20B, 120B), three agent stacks (custom multi-round plan-and-execute, OpenAI Agents SDK, LangChain), and three fault injection conditions, we find that calibrated MCP faults cause severe quality degradation (60\% drop in task completion score, $p < 10^{-56}$) and reveal a scaling paradox: the larger model achieves higher fault-free scores but suffers lower completion rates under faults, driven by longer reasoning chains that amplify fault exposure. Framework-level error handling shapes resilience more than model scale, with stack choice producing significant interaction effects. These results demonstrate that capability benchmarks alone can mask operational brittleness, motivating protocol-aware fault injection as a complementary evaluation dimension for tool-calling agents.

As agent systems scale, skills accumulate into large reusable libraries, yet their scaling laws remain poorly understood. Across 15 frontier LLMs, 1,141 real-world skills, and over 3M routing or execution decisions, we identify two coupled laws. Routing law: single-step routing accuracy decays logarithmically with library size ($R^2{>}0.97$ for all models), with errors progressing from local skill competition to cross-family drift and capture by overly general ``black-hole skills''. Execution law: before state realization, joint routing is approximately multiplicative, whereas correct execution can improve difficult downstream decisions by about $4{\times}$. A single parameter, the routing logarithmic decay slope $b$, couples the two laws: routing-side fits predict execution-side rescue across models, showing that the same library property controls both pre-execution collapse and downstream recoverability. The laws are actionable: law-guided optimization raises held-out routing accuracy from 71.3% to 91.7%, reduces hijack from 22.4% to 4.1%, and transfers directionally to downstream ClawBench and ClawMark execution settings, improving mean pass rate from 49.3% to 61.6% on ClawBench and from 28.4% to 34.5% on ClawMark. These results show that agent performance depends not only on model capability, but also on the structure, granularity, and exposure policy of the skill library.

We study best-arm identification in large-scale stochastic dueling bandits under the sole assumption that a Condorcet winner exists, i.e., an arm that wins each noisy pairwise comparison with probability at least $1/2$. We introduce a new identification procedure that exploits the full gap matrix $\Delta_{i,j}=q_{i,j}-\tfrac12$ (where $q_{i,j}$ is the probability that arm $i$ beats arm $j$), rather than only the gaps between the Condorcet winner and the other arms. We derive high-probability, instance-dependent sample-complexity guarantees that (up to logarithmic factors) improve the best known ones by leveraging informative comparisons beyond those involving the winner. We complement these results with matching lower bounds that establish the optimality of our procedures in all regimes. Overall, our results reveal the general form of the sampling complexity, characterized by a trade-off between the cost of locating informative entries and the verification cost required to achieve the desired confidence. In particular, this complexity drastically differs from what is suggested by pure asymptotic results or by procedures that are tailored to Strongly Stochastic Transitive models.

We study multiple change point localization under bandit feedback. An unknown piecewise-constant function on a compact interval can be queried sequentially at adaptively chosen inputs, and each query returns a noisy evaluation of the function. The goal is to identify a prescribed number of discontinuities, known as change points, within a target precision $\eta$ and confidence level $1-\delta$, while using as few samples as possible. We propose an adaptive algorithm that first detects intervals likely to contain change points and then refines their locations to precision $\eta$. We establish non-asymptotic upper bounds on its sample budget, together with corresponding lower bounds. Prior work shows that jump magnitudes alone determine the asymptotic sample complexity as $\delta\to 0$. We reveal that this picture is incomplete beyond this regime. We demonstrate, both empirically and theoretically, that for general $\delta$ and $\eta$, the complexity is jointly governed by the jumps and the relative positions of the change points.

As high-quality public web corpora become increasingly exhausted, clean long-context documents have become a scarce and expensive source of training data for large language models (LLMs). Existing long-context corpora are often proprietary and costly to acquire, synthetically generated, or concentrated in narrow domains such as programming. We introduce the Stanford EDGAR Filings Dataset (SFD), an open reconstruction of SEC filings into layout-faithful MultiMarkdown for financial language modeling and evaluation. SFD makes audited financial statements, risk disclosures, ownership reports, accounting notes, and market-moving event filings usable as long-context pretraining data and as a basis for financial reasoning, forecasting, compliance, and document understanding. The resulting corpus is token-efficient, model-ready, and has less than 0.1\% overlap with Common Crawl-derived corpora. We release SFD-v1, a 152B-token snapshot of a larger 18.4M-filing corpus estimated at 500B tokens. We further introduce two SFD-derived benchmarks: EDGAR-Forecast, which evaluates filing-grounded numerical forecasting after model knowledge cutoffs, and EDGAR-OCR, which evaluates transcription of complex financial tables.

LLM judges are increasingly used to rank systems and validate claimed improvements, but their validation often reduces to a single question: how well do judge scores correlate with aggregated human ratings? We argue that this question is too fragile. A judge can correlate with human averages while collapsing the rating scale, compressing meaningful margins, or confidently separating systems that humans do not distinguish. Furthermore, a correlation-based workflow always returns a leaderboard, even when the human benchmark cannot support one. We propose a benchmark-conditional measurement framework for LLM-judge validation. Stage 1 audits the human benchmark as a reference ruler using a many-facet ordered Rasch model, estimating rater/item fit, conditional precision, and supported pairwise resolution. Stage 2 calibrates each judge as a linked measurement instrument with judge-specific ordinal thresholds. Stage 3 evaluates out-of-sample system-level fidelity using normalized signed-margin fidelity, false-separation diagnostics, empirical transfer intervals, and distributional EMD. On SummEval and HiTZ-BASSE, the framework yields three outcomes: selection, warning, and refusal, depending on the ruler and judge pool. Calibrated predictions improve observable human-score distribution EMD in 57/81 judge–dimension cases, while the remaining failures are concentrated in the regions flagged by the ruler or judge-instrument audits. Together, these results instantiate benchmark-conditional LLM-judge validation as a protocol for deciding when judge-based claims are warranted, uncertain, or unsupported, addressing a central blind spot of correlation-based judge evaluation.

Inference-time multi-agent LLM scaling lacks a shared unit: counting nominal agents conflates cost with independent evidence. We derive a two-parameter scaling law $R(N) = N_\text{eff}/N = 1/(1+c(N-1)N^{-\beta})$ where the regime exponent $\beta$ classifies any configuration into one of three asymptotic regimes --- hard-ceiling at $1/c$ ($\beta = 0$), sublinear at $N^\beta/c$ ($0 < \beta < 1$), or linear ($\beta \ge 1$), and a mean-field theorem predicts that peer count $k$ and rounds$\tau$ during agent debate enter the dynamics only through their product $k\tau$. The law applies at two levels: answer diversity and correctness redundancy. Across 44 (model $\times$ task $\times$ condition) cells spanning peer debate, self-correction, random-noise placebo, self-consistency, three open-weight families (Qwen, Llama, Ministral) at scales from 7B to 32B with a frontier API check (Gemini), thinking models, heterogeneous teams, and sparse communication, the functional form fits every condition at $R^2 > 0.99$; only $(c, \beta)$ shifts. On free-form math, dense peer influence collapses the answer-level regime from sublinear into hard-ceiling; correctness-level fits remain hard-ceiling throughout. Three findings have practical implications. \emph{(i)}~Thirty dense debating agents produce no more answer diversity than one on MMLU-Hard. \emph{(ii)}~A noise placebo tracks self-correction on free-form math and at $4\times$ scale, so within homogeneous teams the gain commonly attributed to ``debate'' comes from re-evaluation, not peer content. \emph{(iii)}~A single $N \le 5$ pilot predicts the $N=30$ structural ceiling, and within the configurations tested only architectural diversity (heterogeneous teams) lowers $c$ and escapes the hard-ceiling regime, communication-mode interventions do not.

Score- and flow-matching models often rely on preference-based reinforcement learning for two purposes: aligning with subjective preferences and, surprisingly, recovering properties---such as visual realism and coherent object structure---that matching-based training is intended to learn from the data itself. We argue that this reflects a structural mismatch. Matching losses measure $\ell_2$ regression error on the velocity or score field under training-time marginals, a proxy poorly aligned with the visual and semantic properties that determine sample quality at inference. Given a reward aligned with these properties, RL sidesteps the mismatch by evaluating the model on its own samples and following the reward landscape directly. The challenge is to obtain such a reward without relying on human references, which are expensive and conflate data realism with annotator inclinations. We propose Discriminator-Guided RL (DRL). DRL trains a discriminator to separate data from base-model samples in a pretrained representation space and uses its logit as the reward in KL-regularized RL. The pretrained space restricts the discriminator to perceptually meaningful directions, and the logit estimates the log-likelihood ratio between data and model, which is the optimal reward for targeting the data distribution. Across SiT, JiT, REPA, and RAE, DRL reduces guidance-free FID (e.g., $9.38 \to 2.62$ on SiT) and semantic-space FD (e.g., $88.2 \to 19.3$ on DINOv3 for SiT), with consistent gains across all backbones, and improves human-preference rewards without training on them. It also yields a better Pareto frontier between preference reward and image fidelity under subsequent preference-based post-training, increasing alignment while reducing low-level artifacts such as oversaturation and excessive brightness.

Many learning systems are performative: once deployed, their predictions change the data distribution on which future models are trained. In multi-agent settings, this feedback is routed through a network of interacting learners, so stability depends not only on the strength of performativity but also on who affects whom. We study repeated retraining in multi-agent performative prediction and identify a spectral threshold, derived from primitive sensitivity and curvature parameters, that separates stable learning guarantees from worst-case learning obstruction. Prior work often collapses cross-agent feedback into a scalar contraction constant, missing this network structure. We instead introduce the reflexivity matrix $\Gamma$, which records how strongly one agent's deployment can move another agent's best response, defined from per-agent gradient sensitivities and curvature parameters. The threshold occurs when the spectral radius of this matrix crosses one. Below the threshold, repeated retraining converges geometrically to a unique performatively stable equilibrium, with cumulative excess loss bounded uniformly in the time horizon. Above the threshold, we construct supercritical instances in which decentralized learning provably fails: every local-information learner incurs cumulative excess loss that grows linearly in the horizon. Together, these results characterize this threshold behavior through upper and lower bounds and recover the single-agent scalar threshold from prior work as a special case.