NeurIPS 2026 论文列表

Therapeutic target identification---determining which genes or proteins to modulate for a desired clinical effect---is a foundational step in drug discovery. However, existing computational methods that rank candidates at genome scale face three persistent challenges: (i) static feature design, which discards mechanistic knowledge locked in biomedical literature; (ii) lack of interpretability, offering no auditable gene-level rationale for individual predictions; and (iii) the false-negative problem, where unlabeled but potentially druggable genes are treated as definitive negatives. We propose TargetSage, an LLM reasoning agent for therapeutic target identification built on positive-unlabeled (PU) learning, with three modules. First, Agentic Profiling enriches each gene with LLM-gathered biomedical evidence, distilling 2,261 attributes into a 23-dimensional reasoning vocabulary. Second, Guided Reasoning produces gene-specific rationales and explicit reasoning attributes through a GRPO-trained policy rewarded by downstream Adjusted $F_1$. Third, PU-Aware Scoring integrates structured biological features, explicit reasoning attributes, and implicit reasoning embeddings via gated fusion under an LLM-informed class-prior to produce genome-wide druggability scores. Evaluated on 15 benchmark tasks spanning 19,032 human protein-coding genes, TargetSage consistently outperforms ten baselines, surpassing the strongest by 37% in macro-average Adjusted $F_1$ and generalizing to independent held-out benchmarks. Using only 2021 training data, TargetSage outperforms all ten baselines at ranking genes that gain clinical approval by 2025. On an independent validation set of Phase 2/3 clinical candidates, it further achieves a 51% higher enrichment odds ratio at the top-25 shortlist than the runner-up baseline. TargetSage provides an evidence-grounded, continuously updatable foundation for interpretable target prioritization at genome scale.

Scaling laws are increasingly used to pick which expensive training run to scale up. The practical question — which candidate is best at a fixed future compute budget $C^\star$ — fits neither classical best-arm identification nor minimum-oriented functional bandits. We formulate it as the *target-budget best-function identification* (TB-BFI) problem, with two natural flavors: TB-BFI-FC asks for a $\delta$-correct certificate of the winner at $C^\star$, and TB-BFI-FB asks for low target-budget simple regret under a hard exploration budget. For the shared-exponent power-law class we prove uniform target-budget validity, a finite-time stopping bound for the cost-aware TB-LUCB rule, and a per-arm-exponent perturbation extension. We then close two structural gaps. **(a)** We extend the theory *beyond* shared-exponent to the joint $(N,D)$ Chinchilla law with shared but *unknown* exponents, via a two-stage protocol with explicit pilot sample complexity and a quadratic-in-$\epsilon$ centred-design perturbation theorem. **(b)** We give the first positive TB-BFI-FB result: a cost-aware Sequential-Halving algorithm whose cost-weighted simple-regret upper bound matches a new information-theoretic lower bound up to the standard $K \log K$ adaptive-vs-oracle gap. Empirically, theorem-aligned synthetic experiments certify the $C^\star$-winner in $95$–$100\%$ of trials; on a char-level transformer learning-rate-crossover our extrapolator picks the right arm in $3/3$ seeds where Successive Halving picks $0/3$; and on a flip-rank Chinchilla instance the joint-law procedure reaches $70$–$87\%$ accuracy where the univariate $c = N \cdot D$ baseline scores $0\%$.

Object detectors are expected to recognize objects independently of intrinsic object-level attributes, such as scale and local visibility. Yet, natural detection datasets contain systematic intrinsic attribute biases, and modern detectors inevitably entangle these biases within their learned representation geometry, leading to non-uniform performance across the continuous attribute spectrum. This observation exposes a central limitation of conventional data augmentation: while attribute-blind transformations enlarge the training distribution, they fail to explicitly correct the dataset-inherent nuisance structure. To address this, we propose Target-Aware Nuisance Shaping} (TANS, a unified framework that re-purposes data augmentation as an object-conditioned vicinal distribution design. TANS processes training samples through a serial pipeline of two fundamental operators: first, Scale Prior Shaping (SPS) geometrically transports object scales toward low-density regions of the natural scale distribution via class-conditional log-area priors; subsequently, Local Visibility Shaping (LVS) photometrically modulates local foreground-background contrast by constructing smooth, box-conditioned Gaussian fields, strictly preserving the newly established geometry and labels. Furthermore, a feature-state controller monitors the evolving representation geometry to adaptively adjust the shaping strength during training. Extensive experiments on MS COCO and Pascal VOC demonstrate that TANS significantly improves detection performance over strong augmentation baselines across modern architectures. Specifically, on COCO with YOLOv11, TANS achieves a 3.6 mAP gain over the Mosaic baseline. Beyond aggregate accuracy, our attribute-regime analyses, feature-state dynamics, schedule comparisons, and logit-space probes consistently indicate that TANS effectively decouples the detector from intrinsic dataset biases, establishing target-aware shaping as a rigorous paradigm for learning attribute-invariant representations.

How does cooperation evolve in complex agentic systems? Prior work in evolutionary game theory studies why individuals are incentivized to cooperate by isolating social interactions from the physical costs required to execute them, while artificial life models traditionally study the environments that lead to replication without considering the strategic decisions behind the evolved mechanisms. In contrast, we introduce **Autopoietic Game Theory**, a computational model where social interactions, replication mechanisms, and their associated computational costs are endogenous and simultaneously co-evolving. We study these dynamics using a computational substrate of randomly initialized programs in Z80 machine code, showing empirically and theoretically that embedding a social dilemma directly into the physics of computation can favor the emergence of self-replicating, cooperative strategies. When resources are scarce, our analysis shows that *defection can become self-limiting even in well-mixed populations*: parasitic stealing destroys shared energy, slows execution, and can prevent reliable replication. Empirically, evolved programs suppress stealing across several Z80 environments, while spatial assortment further supports structural complexity and task performance. We further show that the framework can incorporate exogenous pressures, such as math tasks structured as sequential social dilemmas, when rewards are tied to computation budgets. These results suggest that coupling an agent's capacity for computation to its available energy transforms cooperation into a dominant scaffolding for building complex, sustainable, and self-organizing systems.

Concept erasure aims to remove a target attribute from a representation while preserving the other information encoded in it. This is difficult beyond the linear setting: a target signal hidden from one probe may remain recoverable by a fresh nonlinear probe, while unconstrained nonlinear updates may remove the target by pushing representations off the manifold of natural hidden states. We propose the *Manifold Representation Hypothesis* (MRH): natural hidden states concentrate near a structured, lower-dimensional manifold, so surgical erasure should act along local manifold degrees of freedom rather than arbitrary ambient directions. We operationalize this hypothesis with the TanGCE family of erasure methods. The core method estimates each representation's local tangent space from nearest-neighbor secants, projects a nonlinear concept-scorer gradient onto that tangent, and applies a per-sample trust-region step; TanGCE+ and TanGCE++ prepend closed-form first- and second-moment erasers before the same manifold-constrained loop. Across 119 settings spanning 13 language models, three NLP concepts, and 40 CelebA-CLIP attributes under two control regimes, TanGCE++ removes more target signal than the strongest published baseline at matched control-damage budgets under one fixed hyperparameter recipe. Applying the same manifold-constrained loop after prior erasers also consistently reduces residual nonlinear leakage without leaving the control budget, empirically supporting MRH's operational prediction that manifold-constrained edits are a useful inductive bias for surgical concept erasure.

Predictive models on structured tabular data often face imbalanced targets: training emphasizes common values while rare outcomes stay underrepresented. Distributionally robust optimization hedges against shift, but standard ambiguity sets use a single radius over the entire distribution, so adversarial mass concentrates on dense regions and rare target ranges remain weakly protected; worst-case statements then track bulk behavior rather than tails. We introduce target-conditional distributionally robust optimization (TC-DRO), which defines ambiguity per target region with radii that grow where data are scarce. We establish coverage, per-region excess risk that tightens with local sample size when standard DRO yields vacuous tail guarantees, and a dual as weighted empirical risk with theory-derived density-adaptive weights. Experiments on seven tabular benchmarks show that Wasserstein DRO collapses to empirical risk minimization under uniform weights, while global $\chi^2$-DRO can remain close to ERM in practice; TC-DRO improves tail MSE and SERA relative to both standard training and recent imbalanced-regression methods.

Multi-agent reinforcement learning (MARL) holds great potential but faces robustness challenges due to environmental uncertainty. To address this, distributionally robust Markov games (RMGs) optimize worst-case performance when the environment deviates from the nominal model within a uncertainty set. Beyond robustness, an equally urgent goal for MARL is data efficiency—sampling from vast state and action spaces that grow exponentially with the number of agents potentially leads to the curse of multiagency. However, current provably data-efficient algorithms for RMGs are limited to tabular settings with finite state and action spaces, which are only computationally manageable for small-scale problems, leaving RMGs with large-scale (or infinite) state spaces largely unexplored. The only existing work beyond tabular settings focuses on linear function approximation (LFA) for a restrictive class of RMGs using vanish minimal value assumption and still suffers from sample complexity with the curse of multiagency. In this work, we focuses on general RMGs with LFA. For uncertainty sets defined by total variation distance, we develop provably data-efficient algorithms that break the curse of multiagency in both the generative model setting and a newly proposed online interactive setting. To our knowledge, our results are the first to break the curse of multiagency of sample complexity for RMGs with large (possibly infinite) state spaces, regardless of the uncertainty set construction.

Molecular docking aims to predict the three-dimensional structure of a protein–ligand complex; in the conventional structure-based setting, it is conditioned on an input protein structure and ligand, often starting from an apo receptor in flexible docking or a holo receptor in rigid docking. Recent co-folding models exemplified by AlphaFold 3 achieve strong accuracy in protein–ligand complex prediction, but they typically generate complexes from sequence-derived inputs and therefore do not directly use apo structures as structural priors in docking, leading to a larger conformational search space and weaker robustness in difficult or few-step settings. We present BridgeDock, a framework that adapts pretrained co-folding backbones to flexible docking by modeling the apo-to-holo transition as a diffusion bridge, allowing generation from the initial protein–ligand state rather than from Gaussian noise. To make this bridge formulation compatible with pretrained denoisers, we introduce an alignment mechanism that maps bridge states to the original denoising schedule of the pretrained model. Experiments on standard flexible docking benchmarks show that BridgeDock consistently outperforms strong co-folding baselines. Moreover, BridgeDock is computationally efficient at inference time and remains effective even with very few denoising steps.

Reinforcement learning (RL) has substantially improved the reasoning capabilities of vision-language models (VLMs), yet it often triggers chain-of-thought (CoT) obfuscation, a regime where models achieve high accuracy while producing reasoning traces that are ungrounded and difficult to monitor. While prior work documents this decay at the behavioral level, the underlying mechanistic drivers of such representational drift remain poorly understood. In this work, we identify that obfuscation is a representational pathology, where RL-induced optimization causes task-agnostic template features to replace visually grounded content in the model’s activation space. Based on this insight, we propose TAME, an activation-level intervention framework that leverages Sparse Autoencoders (SAEs) to regularize VLM internal features during RL training. Specifically, TAME utilizes an LLM-as-judge monitor to detect behavioral obfuscation and maps these signals to specific internal directions, penalizing template intrusion while preserving visual evidence. By directly intervening on the features responsible for obfuscation, our method restores the transparency and visual grounding of reasoning traces. Experiments on the VIRL-39k and SPA-VL benchmarks across two model families show that TAME improves CoT monitorability (by up to +30.9% on VIRL and +16.7% on SPA-VL over GRPO), while preserving task accuracy and general-capability performance across four standard VLM benchmarks.

Decentralized Stochastic Approximation (SA) enables cooperative fixed-point solving over mesh networks without a central server. In these systems, local update is relatively cheap compared with coordination, and frequent information exchange makes communication cost the main bottleneck. To alleviate this issue, we propose a decentralized SA method in which each agent performs $H$ local updates before each communication round. By amortizing communications over multiple local SA updates, the method can substantially reduce communication cost. We also study a multiple mixing scheme based on FastMix, which trades a small amount intra-round communications for a stronger consensus effectiveness on poorly connected graphs. Theoretically, we establish finite-time bounds under general contractive norms and Markovian sampling, by combining contraction, consensus error, and delayed Markovian terms in a round-level Lyapunov recursion. The results show trade-offs among computation, communication and sampling. Numerical experiments on decentralized Q-learning, Markovian SGD and linear SA validate the theory and show substantial communication savings for target accuracy.

Schedule-Free SGD, proposed in *The Road Less Scheduled*, achieves optimal convergence rates without requiring the training horizon in advance, by replacing learning rate schedules with a principled form of iterate averaging. However, the method still requires tuning a base learning rate whose optimal value depends on unknown problem constants. In this work, we continue down this road by deriving Polyak-type step sizes for Schedule-Free SGD and Adam that compute the learning rate at each iteration from the sampled loss, gradient, and current iterates alone. We first propose an oracle variant that uses per-sample optimal function values and prove an $\mathcal{O}(1/\sqrt{t})$ anytime last-iterate rate for convex Lipschitz objectives. We then remove the oracle requirement with a safeguarded variant that replaces the unknown optimal values with any available lower bound, achieving the same rate up to a neighborhood that vanishes under interpolation. Both step sizes reduce to existing Polyak rules for standard SGD when momentum is set to zero, unifying standard and schedule-free Polyak methods. Numerical experiments on language modeling, including pretraining and distillation, show that the proposed methods match or surpass tuned Schedule-Free baselines while offering greater robustness to hyperparameter choices.

While optimal transport (OT) enforces a rigid constraint by requiring two measures to be matched exactly, partial optimal transport relaxes this requirement by allowing mass to remain unmatched through a global budget, scalar rebate, or uniform rejection rule. However, many applications call for more structured, pointwise rejection mechanisms, where the decision to leave mass unmatched depends on side-specific reliability, support geometry, or external information about which components should participate in the comparison. We introduce \emph{intent-controlled partial optimal transport} (IC-POT), a targeted generalization of partial transport that replaces the global rejection paradigm with pointwise rejection costs over both measures. We show that the resulting optimization problem admits a dual interpretation in terms of local acceptance thresholds and can be solved by recasting it as a balanced Kantorovich OT problem on an augmented support. Beyond theoretical analysis, we demonstrate the practical relevance of IC-POT in settings where rejection is driven by side information. In positive-unlabeled learning and open-partial domain adaptation, incorporating pointwise rejection rules that encode statistical structure improves fixed baseline pipelines. Finally, we motivate the use of IC-POT with a geophysical practical case: multi-modal satellite ocean measurements, for which physical and instrumental priors naturally inform the rejection mechanism and define the retrieved comparable signal information.

Safe preference alignment of large language models must handle rare harmful tails whose burden is concentrated on demographic, topical, linguistic, or adversarial subgroups. Existing coverage analyses operate on global density ratios that can look adequate on average while the harmful tail of a small group remains unsupported, leaving worst-group safety risk unidentified. We assume that the absolute harm scale is externally calibrated by severity labels or anchors and introduce TailGuard, a framework centered on subgroup quantile coverage $C_{g,\tau}$: the worst deployment-to-training density ratio restricted to group g's upper-τ harm tail. We prove that $C_{g,\tau}$ is a key statistical quantity governing subgroup tail safety: it governs tail-risk transfer, yields an offline impossibility result under missing tail support, and is necessary for fixed-policy subgroup tail-risk estimation. From a per-group CVaR_τ-constrained alignment objective, we derive a KL-regularized Gibbs form, motivate a Tail-Fisher active query rule that targets group-tail coverage deficits, and construct a per-group weighted CVaR risk-control deployment gate. We validate the mechanism through four completed data-level studies: synthetic coverage control, a semi-synthetic tail-hiding pilot on real safety-alignment labels, an observed-label active-query ablation, and an oracle-score risk-control-gate efficiency proxy. These experiments test the coverage mechanism and deployment diagnostic.

Long-term time-series forecasting (LTSF) underpins applications such as energy-load prediction and traffic-flow forecasting, yet model performance often deteriorates as the horizon extends. In particular, errors accumulate with lead time and systematically amplify toward the end of the predicted sequence, a phenomenon known as tail deterioration. We posit that this instability is driven by weakly constrained fusion of cross-temporal and cross-scale context: aggressive mixing can propagate noise and early-stage bias into distant horizons, ultimately degrading tail accuracy. To address this issue, we propose TailFix, a controllable cross-scale mixing framework for long-horizon forecasting. TailFix selectively gates interactions among multi-scale representations, derives scale-level correction terms from contextual summaries, and injects them via gated residual connections to suppress error diffusion and improve long-horizon stability. We further incorporate adaptive channel enhancement and a lightweight tail-correction module to explicitly refine the error-prone tail region. Finally, we introduce the Tail Amplification Factor (TAF) to quantify tail error amplification relative to the early portion of the sequence. Across diverse benchmarks, TailFix consistently mitigates tail error amplification and improves performance on multiple long-horizon forecasting tasks.

Generative models are increasingly used to simulate risk-sensitive systems, from financial markets and energy demand to healthcare, where downstream decisions depend on rare but consequential events. In this setting, matching average behavior is not enough: rare tail events drive stress testing, capital allocation, and policy analysis. Yet tail objectives such as Value-at-Risk and Expected Shortfall are difficult to optimize directly because empirical estimators rely on sorting, thresholding, and subset selection. We introduce TailDiff, a training-free inference-time guidance method for pretrained diffusion models that uses jointly elicitable Fissler-Ziegel scoring rules to construct differentiable tail-aware objectives. We analyze the induced guidance field locally in the low-noise regime, characterizing tail-shaping and boundary-coupling behavior together with controlled perturbations under small realized effective drift. On synthetic data, TailDiff approaches the oracle finite-sample floor, on a challenging financial tail-risk simulation benchmark, it outperforms the state-of-the-art on tail metrics while preserving bulk structure.

Long-tailed recognition remains challenging not only because rare classes provide limited supervision, but also because their evidence is difficult to accumulate during standard stochastic training. We characterize this failure as tail signal erosion: in conflict-dominant regimes, frequent-class updates can dilute sparse rare-class signals before they are sufficiently reinforced. To address this issue, we propose TailCon, a memory consolidation framework that separates rare-evidence acquisition from parametric integration. During online acquisition, TailCon stores uncertain or rare representations in detached episodic memory, while a parametric prediction pathway learns global decision structure. To avoid relying only on test-time retrieval, TailCon introduces FOCUS, a periodic consolidation stage that distills stored memory evidence into the parametric prediction head through a low-capacity alignment surrogate. We provide a local optimization analysis that scopes when this separation is beneficial in sparse-recurrence, conflict-dominant regimes. Experiments on CIFAR-100-LT, CIFAR-10-LT, ImageNet-LT, and iNaturalist 2018 show consistent improvements over representative rebalancing, decoupled, expert-based, and prototype-oriented baselines under the evaluated protocols. Memory-disabled inference further shows that part of the stored evidence is transferred into the parametric prediction head rather than remaining only in the retrieval branch. An anonymized implementation is included in the Supplementary Material.

Long-Tailed Class-Incremental Learning (LT-CIL) is challenging due to severe class imbalance, where frequent head classes dominate gradient updates and suppress learning for rare tail classes. Beyond data imbalance, we identify an asymmetric likelihood signals within each session: tail classes contribute insufficient evidence to overcome prior regularization, causing their parameters to remain near the prior mean even under variational inference. This asymmetry disrupts the stability-plasticity tradeoff, leading to pronounced forgetting and poor tail-class performance. We propose a hierarchical variational framework, TailAdapt, for prompt and adapter-based continual learning that enables adaptive group-wise regularization of model parameters. TailAdapt employs a hierarchical inverse Gaussian scale-mixture prior, which induces a heavy-tailed marginal distribution over prompt and adapter parameters. This formulation encourages selective plasticity so that most parameter groups are strongly regularized to preserve previously learned knowledge, while a small, data-supported subset is allowed to adapt substantially. This selective plasticity allocates adaptation capacity where it is most needed, mitigating interference from head classes and improving learning for tail classes. Extensive experiments on LT-CIL benchmarks demonstrate consistent improvements over strong baselines, with better tail-class performance, reduced forgetting, and more efficient utilization of prompt and adapter capacity.

We study Stackelberg learning in which followers use lower-tail CVaR as a reward utility. In cooperative bandits, where both agents share the CVaR objective, we prove a spectral-risk lower bound that gives $\Omega(\sqrt{K(AB-1)/\tau})$ for CVaR and give a Stackelberg-factorized CVaR-UCB algorithm with matching $\widetilde O\sqrt{ABK/\tau})$ regret up to logarithmic factors. Thus CVaR learning admits a minimax-optimal realizable response certificate in bandits. In Markov games, a risk-neutral leader faces a CVaR-sensitive follower. We introduce a certified follower-response oracle based on shortfall planning and decompose true Stackelberg regret into certified oracle regret and benchmark response certification error. The first term is sublinear without coverage, while true regret requires comparison coverage and response compatibility.

Best-of-N sampling is a simple inference-time scaling strategy for large language models, yet its behavior under incomplete reward models remains poorly understood. We study Best-of-N as a competition between the extreme reward tails of correct and incorrect responses. This view shows that success depends not only on the probability of sampling a correct response, but also on whether correct responses dominate incorrect ones in the upper tail. Using extreme value theory, we characterize three asymptotic regimes: correct-tail-dominant, incorrect-tail-dominant, and critical explaining both monotonic gains and reward-hacking-induced degradation. We further derive finite-sample scaling laws for representative reward distributions, showing that, under Gaussian rewards, incorrect responses with sufficiently large variance can dominate selection even when their mean reward is lower. Unlike oracle selection, convergence with an incomplete selector can be polynomial rather than exponential. Finally, we propose a lightweight tail-tension criterion to estimate when additional sampling may become harmful. Experiments on synthetic data and real reward model scores validate the predicted regimes, scaling laws, and diagnostic behavior, suggesting that tail competition provides a unified lens on Best-of-N sampling for verifiable problems.