NeurIPS 2026 Papers

In cooperative multi-agent reinforcement learning under partial observability, agents typically differ in both their local observations and in how much value those observations let them recover. Most existing methods treat agents symmetrically when shaping credit and exploration, ignoring this asymmetric information disadvantage. We introduce the per-agent value gap, a state- and agent-specific quantity that measures the value loss attributable to local information limits, and show that it serves as a single, measurable signal that unifies credit assignment and exploration: down-weighting policy updates where an agent is at a strong disadvantage, and rewarding behaviors that reduce the disadvantage along the trajectory. We instantiate this idea as \method{}, which estimates the gap online with a per-agent local value head and uses it to drive both advantage shaping and an information-seeking intrinsic reward. We further show that the resulting shaping reduces the variance of the policy-gradient estimate at high-disadvantage states under a mild correlation assumption, and that the intrinsic bonus vanishes in expectation under any stationary optimal policy, so the augmented objective remains consistent with the original cooperative objective. On sixteen tasks across five cooperative benchmarks, \method{} ranks first or statistically tied for first on fifteen of sixteen tasks (strictly first on twelve), with the largest gains on settings combining sparse rewards and asymmetric observability (e.g., a $24\%$ mean relative gain over the strongest baseline averaged across the three SMAC v2 tasks).

Natural gradient descent is a theoretical pillar of second-order optimization for neural networks. Existing implementations either rely on structural approximations of the curvature matrix or incur substantial computational and memory overhead. Direct application remains difficult at deep-network scale. We build an optimizer based on an unbiased stochastic estimator of the Fisher information matrix. It employs a truncated exponential moving average metric with damping, which we call the Stochastic Truncated Metric (STM), and is otherwise closely aligned with the original natural gradient. This gives a matrix-free parameter update with strictly $O(Kd)$ time and memory per step, where $K$ is a small constant. It is applicable across network architectures without layer-wise assumptions. We bound the error induced by the finite-memory truncation. We show that STM is stable and efficient on vision and language benchmarks.

In materials-science papers, quantitative evidence—XRD peak shifts, Raman ID/IG ratios, high-frequency Z′ intercepts in EIS—lives in line plots. Hosted vision–language models describe these plots fluently but cannot recover the (x, y) values an agent needs to query, compare, or integrate. We release three coupled artifacts. MatCurvs is a real-coordinate chart benchmark for materials-science spectra, organized in three tiers spanning XRD, Raman, XPS, XAFS, EIS, GCD, and CV: 1,375,165 classified subfigures from 55,763 articles (L0); 204 panels with manual per-curve pixel polylines (L1); and 8,026 verified real-coordinate curves over 3,817 panels (L2). MatDeplot is a local extractor that recovers 45.8% of curves within a 5-pixel IoU tolerance against L1 ground truth, versus ≤1.2% for the strongest hosted VLM—a 38× pixel-anchoring gap, closed in roughly 1.5 seconds per image. MatCurvs-Reasoning is a 14,740-question deterministic numerical evaluation: feeding MatDeplot’s extracted (x, y) data to an LLM cuts median relative error from 54% for an image-only VLM to about 4%, and a text LLM with the data alone matches the same VLM prompted with both image and data. By turning each published chart into a numerically queryable curve, the released artifacts let a deployer compute derived materials parameters—Raman ID/IG disorder ratios, XRD Scherrer crystallite sizes, and phase-purity audits—directly from a literature-scale chart corpus, where most papers under-report these numbers and the chart is the only available record.

Test-time adaptation updates a deployed model using only unlabeled target data, but standard entropy-based methods often rely on deterministic confidence and can reinforce overconfident errors under distribution shift. We propose MIRA, Mutual Information guided calibration for Reliable Test-Time Adaptation, a source-free online adaptation method that uses lightweight last-layer Gaussian perturbations to probe prediction reliability. For each target batch, MIRA reuses a single feature extraction pass and samples perturbed classifiers around the source classifier, forming an efficient local stochastic ensemble. Rather than fitting a Bayesian posterior, MIRA treats the perturbation scale as a local probing radius and selects a batch-specific scale using mutual information and prediction agreement. The resulting perturbation-sensitivity signal identifies predictions that are confident and locally stable under classifier perturbations, while suppressing brittle predictions with high stochastic disagreement. MIRA then performs adaptation with an MI-confidence weighted entropy loss, which down-weights perturbation-sensitive or low-confidence samples before they drive entropy minimization. This weighted loss reduces the influence of brittle predictions that can otherwise cause harmful adaptation under distribution shift. The method requires no source data, retraining, model ensembles, or offline posterior fitting, and can be applied as a plug-and-play modification to entropy-minimization TTA. Experiments on distribution-shift benchmarks show improvements in accuracy, calibration, and negative log-likelihood, suggesting that mutual-information-guided perturbation probing and weighted adaptation yield more robust online TTA than deterministic confidence.

The performance of machine learning models often relies on large labeled datasets; however, data collected from diverse sources can contain label noise. Recent work has shown that, in noisy settings, there may exist a subset of the training data on which models can achieve performance comparable to training on a noise-free dataset. A widely used method for identifying such subsets is \textit{cutstats}, which employs $k$-nearest neighbors ($k$-NN) to detect low-noise samples. However, its performance on high-dimensional data remains largely unexplored. In this work, we formally establish that the performance of a classifier trained on a subset of a noisy dataset selected via \textit{cutstats} is influenced by the accuracy of $k$-NN. We further demonstrate that, in noisy environments, exploiting data invariance and knowledge of underlying symmetries can significantly enhance the performance of $k$-NN, bringing it closer to the Bayes optimal classifier even in high-dimensional regimes. Finally, we show that for real-world scenarios, where information about the underlying invariance is only partially known, learnt invariant representations can still facilitate the identification of near-optimal subsets.

This paper studies adaptive targeting under network interference in a bandit setting, where treatments applied to one individual may affect others through spillover effects. We consider a linear model in a sparse regime, where each individual's outcome can be affected by at most a few others. We first establish a regret lower bound showing that ignoring the network structure and reducing the problem to a standard linear bandit inevitably leads to inefficient learning, particularly in large populations. To understand how structural information can be leveraged, we analyze regimes with varying levels of knowledge of the interference structure: (1) full support knowledge, (2) knowledge of the column support sizes, and (3) no prior knowledge. For each regime, we establish regret lower bounds characterizing the fundamental limits of learning, and develop algorithms that achieve near-optimal regret. Together, our results provide a unified view of how knowledge of the interference structure governs the efficiency of online learning under interference, and offer practical adaptive targeting algorithms in each setting. Numerical experiments on synthetic and real-world data demonstrate the practical benefits of our algorithms.

When does a language model acquire a capability? When its hidden states encode the relevant information, or when its output readout can express it? We investigate this gap between representational availability and readout expression by tracking the unembedding matrix through pretraining. Because unembedding rows are aligned by token identity across checkpoints, they provide one trajectory per vocabulary item through the learned output interface. To measure these trajectories, we introduce parameter-trajectory crosscoding, a checkpoint-spanning sparse dictionary with shared feature identities and checkpoint-specific decoders. Applied across Pythia scales and in OLMo-2-7B, this method reveals an early, heterogeneous reorganization of the readout. This reorganization does more than alter structure. Our independent WordNet probes show that token families become increasingly separable over the same developmental window. We then causally test whether the readout component drives this development. By swapping the unembedding matrices across checkpoints, and controlled contrastive tasks, we show that task-relevant distinctions can be present in hidden states before the contemporaneous readout expresses them in logits. By ablating individual crosscoder features, we trace these delayed readout effects directly to compact, learned directions. More broadly, developmental analyses should distinguish representational availability from readout expression. Together, our findings demonstrate that the learned output interface helps determine when latent structure becomes visible in token logits, highlighting the need for developmental analyses to explicitly decouple hidden-state representation from readout expression.

We study a repeated information design setting in which the receiver, who is also the decision-maker, updates beliefs in a systematically biased way. More specifically, a distorted posterior in our model can be written as a convex combination of the prior and the Bayesian posterior, governed by a fixed but unknown parameter. Over repeated interactions, the sender chooses persuasive signaling schemes, observes only the receiver’s realized actions, and seeks to minimize regret relative to a full-information oracle that knows the receiver’s biased updating rule. We propose a safe exploration algorithm for learning the receiver’s bias while maintaining high persuasion value. The algorithm exploits the asymmetric cost of probing: conservative probes incur only local loss, whereas overly aggressive probes may lose the persuasive opportunity entirely. For general finite state and action spaces and arbitrary bounded utilities, our method achieves $O(\log\log T)$ regret. A matching $\Omega(\log\log T)$ lower bound shows that this rate is optimal. We further discuss the influence on receiver welfare, as well as extensions to jointly unknown prior and bias, and contextual settings with time-varying priors and utilities.

Animals integrate information across sensory modalities to guide exploration, suggesting that they maintain internal estimates of where actions are expected to reduce uncertainty. However, how such action-conditioned information maps are learned from multimodal sensory experience remains unclear, as previous studies have been largely confined to unimodal sensing frameworks. We introduce a multimodal predictive-coding framework for learning uncertainty-reduction maps from visual and mechanosensory observations in hawkmoth flower interaction tasks. A variational generative model trained on simulated environments recovers modality-specific information landscapes over the action space, capturing how visual patterns, surface geometry, and mechanosensory cues shape the value of exploratory actions. These learned maps account for several features of hawkmoth behavior, including angled offsets during flower tracking, probing along visual patterns, and geometry-dependent changes in exploration. Applying the model to behavioral data from flower tracking and nectary search, we find that natural trajectories tend to occupy regions of high uncertainty reduction in these learned maps. Building on these observations, we develop an uncertainty-gated lexicographic algorithm that switches between information gathering and reward exploitation based on perceptual uncertainty. Across downstream reward-learning tasks, this policy learns faster, is more sample-efficient, and generalizes better to unseen environmental conditions than exploitation-only policies, $\epsilon$-greedy exploration, and non-lexicographic baselines. Together, these results suggest that multimodal generative perception models can learn actionable information landscapes that explain biological sensing and support exploration in embodied decision-making.

Choosing a Large Language Model (LLM) for a given task requires comparing many strong candidates, yet standard evaluation relies on costly annotations over fixed evaluation sets. To address this challenge, we develop Select-LLM, the first framework for active model selection of LLMs. Select-LLM aims to find a small set of queries whose annotations are most informative for identifying the best LLM for a given task. To this end, we introduce a query selection rule based on expected information gain, computed from pairwise similarities between candidate model outputs. Because this rule only uses generated model responses, Select-LLM can be applied across candidate models without assumptions about their architecture or access to model weights. This makes it suitable for both open-weight and black-box LLMs. We evaluate Select-LLM across 23 datasets, 156 evaluated models, diverse task families, and multiple text evaluation metrics. Across all experiments, Select-LLM improves over the strongest baseline in every setting, with annotation cost reductions up to 81.8% for best model selection and up to 84.78% for near-best model selection.

Mutual information (MI) is fundamental to representation-learning analysis in deep networks, but in high dimensions MI estimators depend on hyperparameters --- bin widths, kernel bandwidths, neighbourhood radii, auxiliary networks --- whose bias often dominates the signal. We show that continuous piecewise-linear (CPWL) networks, including ReLU networks, admit an intrinsic information-theoretic analysis that avoids this pitfall: their inherent partition of the input space into convex linear regions assigns to every input a discrete region label $\Pi$ alongside the continuous representation $T$. We decompose $I(Y; T)$ exactly into geometric terms, where the \emph{routing information} $I(Y; \Pi)$ admits a hyperparameter-free plug-in estimator that approximates a population lower bound on every other MI term in the decomposition. When the partition saturates, a functional-equivalence quotient at relative Frobenius tolerance $\varepsilon \in [0, 2]$ collapses regions implementing the same linear operator and restores informativeness. Empirically, the routing estimator correlates strongly with three established MI baselines {\it without any hyperparameter}, and the functional quotient at moderate $\varepsilon$ recovers informative estimates in the regime where the raw partition is bounded by $\log_2 N$ rather than $H(Y)$. We provide a parameter-free framework for analysing the internal information geometry of CPWL networks, grounded in their inherent piecewise-linear structure rather than in external discretisation.

Generative Flow Networks (GFlowNets) have emerged as a flexible framework for amortized inference over discrete and mixed discrete-continuous objects, requiring only an unnormalized target density specified through a reward. In this work, we formulate forward-policy training in GFlowNets through the information geometry of the induced trajectory sampler. Treating the forward policy as an induced trajectory sampler, we show that its intrinsic first-order geometry is given by the Fisher-Rao metric of the trajectory family, and that the associated natural gradient provides the canonical local update whenever the corresponding Fisher information is computable or accurately approximable. We derive an exact decomposition of the trajectory Fisher into per-step conditional second moments, which clarifies when temporal score interactions vanish and when dense couplings remain under shared parameterization. This leads to three computational regimes: settings with tractable exact Fisher information, settings where Monte Carlo estimators of the expected Fisher are sufficient, and structure-exploitable settings in which target locality or factorization yields accurate approximations of the Fisher expectation. In the latter case, graphical-model tools such as exact marginalization, separator methods, and belief propagation provide principled surrogates for natural-gradient updates. The resulting framework turns target structure into optimization geometry and yields a tractable route to structure-aware forward-policy training in GFlowNets. We illustrate the framework empirically through examples comparing convergence and exploration behavior under Riemannian and Euclidean optimization.

The information bottleneck offers a candidate general framework for learning, defined by two axes: how much a representation preserves about the external world and how much it carries about the relevant target. However, empirical evidence for information-plane dynamics in realistic-scale artificial networks and in biological systems remains limited. We address this by tracking information-plane trajectories during learning in three systems with different substrates, using a matched analytical pipeline. First, in a gradient-trained spiking ResNet-18 on CIFAR-10 --- a biologically plausible artificial substrate --- where a two-phase fitting-then-compression trajectory emerges specifically in the deep layers; the first information-plane analysis of a spiking network at this scale. Second, on the open-source dataset of mouse visual cortex during multi-week category learning, where our pipeline recovers the published cohort-level effect now reformulated in IB-plane terms. Third, in our own single-photon miniscope calcium imaging data of mouse hippocampus during multi-day place learning in open field, where the IB axes map onto the well-established egocentric-to-allocentric transformation: the input axis becomes egocentric sensorimotor state, the output axis becomes allocentric position. Behavioral and decoder-baseline controls confirm the hippocampal trajectory reflects experience-driven learning. Across all three systems, we observe coherent motion along the relevance axis with learning, while compression emerges only where task structure demands it --- most pronounced in the SNN. The information bottleneck plane therefore offers a substrate-independent coordinate for representation learning, even as the strength of compression along it remains task-dependent rather than universal.

Active feature acquisition (AFA) is an instance-adaptive paradigm in which, at inference time, a policy sequentially chooses which features to acquire (at a cost) before predicting. Existing approaches either train reinforcement learning policies, which deal with a difficult MDP, or greedy policies that cannot account for the joint informativeness of features or require knowledge about the underlying data distribution. To overcome this, we propose Template-based AFA (TAFA), a non-greedy framework that learns a small library of feature templates---sets of features that are jointly informative---and uses this library of templates to guide the next feature acquisitions. Through identifying feature templates, the proposed framework not only significantly reduces the action space considered by the policy but also alleviates the need to estimate the underlying data distribution. Extensive experiments on synthetic and real-world datasets show that TAFA outperforms the existing state-of-the-art baselines while achieving lower overall acquisition cost and computation.

LLMs exhibit uneven performance across programming languages, yet benchmark gaps often reflect narrow task regimes and evaluation-specific confounds. This makes it hard to tell whether poor performance reflects a broader weakness with a given language, a broader difficulty on a given task, or a limitation specific to that task-language combination. We adapt Information Parity, a measure of relative cross-language representational efficiency developed for natural language, to code. We define Code Information Parity (Code IP) on a controlled parallel corpus of 22 algorithms across 20 languages, audited to preserve core semantics while minimizing boilerplate and excluding library shortcuts. The results are regime-specific: Code IP correlates strongly with multilingual benchmark performance on short-horizon generation ($\rho$ from $-0.55$ to $-0.81$), remains negative though moderated on execution prediction ($\rho=-0.429$), and disappears on whole-program synthesis ($\rho=+0.105$). Rather than a universal metric of multilingual coding ability, Code IP isolates a reliable cross-language signal on short-horizon generation and delineates where that signal no longer separates cleanly from broader task and evaluation effects.

An extensive line of work studies fairness interventions for network embeddings, but less is known about their baseline behavior. In this work, we ask: how do baseline embeddings (without fairness interventions) produce disparate effects at the representation level? We analyze the asymptotic behavior of low-dimensional embeddings on stochastic block model (SBM) graphs, which encode both homophily and group structure. We characterize exact conditions under which embeddings cause information loss, showing that the amount of information loss depends directly on the graph’s density and assortativity. Notably, very different graphs can produce identical embeddings in the limit, and this non-invertibility disproportionately affects smaller and sparser communities. As a result, simple downstream tasks, such as link prediction, introduce higher error rates for these communities, helping explain disparities widely observed in practice.

LLMs are increasingly used with external knowledge sources like the Internet. Do they weigh information appropriately---updating more for reliable sources (source discernment) and more when claims bring priors closer to the truth (truth discernment)? We formalize this as information discernment and introduce Learn2Discern (L2D), an experimental framework and benchmark grounded in three normative axioms with interpretable metrics. To establish external validity, a pre-registered, quota-matched user study (n=299) confirms that real LLM users endorse all three axioms and report that violations reduce their trust and usage intent. Across 13 models and nearly 670K trials, we find consistent failures across both dimensions: models perform near chance on source and truth discernment, rely on source popularity twice as much as source reliability, and update roughly equally whether a claim improves or worsens their position relative to the ground truth. Models integrate external knowledge most effectively on datasets where their priors are already the most accurate. Newer and larger models improve truth discernment but not source discernment, a blind spot that model complexity does not address. We identify simple inference-time interventions that meaningfully improve both forms of discernment. We release our dataset, metrics, and survey as a testbed for a core alignment property that scales in importance as LLMs replace traditional search.

Exploring high-order correlations and long-range dependencies in brain networks holds significant value for both neuroscience research and clinical diagnosis. However, previous studies have lacked a unified integration of high-order and long-range dependency information in brain networks, and there is substantial redundancy behind various types of information. These issues limit their effectiveness in the diagnosis of brain diseases. To address this, we propose an Information Bottleneck- Guided Adaptive HyperGraph Transformer (IBAHGT). By incorporating the information bottleneck (IB) principle, this approach enables adaptive learning of high-order correlations and both short- and long-range dependencies within a unified framework for brain network analysis, achieving high-precision brain disease diagnosis. IBAHGT consists of three key components: an information bottleneck- guided adaptive hypergraph convolution, which introduces a novel hypergraph information bottleneck (HIB) principle to adaptively learn hypergraph message- passing weights between nodes and hyperedges, optimizes information flow and captures high-order information in brain networks that is maximally informative and minimally redundant (MIMR). The Transformer encoder captures global information within brain networks through the attention mechanism, specifically modeling short- and long-range dependencies. An information bottleneck-guided node-level adaptive fusion employs the IB principle to learn independent weights for each node, facilitating the fine-grained integration of high-order information and global information to obtain an efficient representation for downstream tasks. Extensive experiments demonstrate that the proposed method outperforms current state-of-the-art methods and can identify biomarkers for clinical applications.

Supervised fine-tuning (SFT) provides the standard approach for teaching LLMs new behaviors from offline expert demonstrations. However, standard SFT uniformly fits all samples—including those with low likelihood under the base model—which can disproportionately drive training updates toward overfitting specific samples rather than learning the target behavior. Moreover, adapting to these unlikely samples induces substantial policy shifts that degrade prior capabilities. Existing methods mitigate this by filtering, regenerating, or down-weighting low-likelihood data. In doing so, they often suppress precisely the novel behaviors the base model has yet to learn. We propose InfoSFT, a principled weighting scheme for the SFT objective that concentrates learning signals on maximally informative, medium-confidence tokens—those neither overly familiar to the base model nor too unlikely to cause instability. Requiring only a one-line modification to the standard token-wise loss, InfoSFT demonstrably improves generalization over vanilla SFT and likelihood-weighted baselines across math, code, and chain-of-thought tasks with diverse model families, while better preserving pre-existing capabilities.