NeurIPS 2026 论文列表

Change Detection & Captioning (CDC) aims to jointly localize changed regions and describe their semantic content from bi-temporal remote sensing images. While recent approaches have demonstrated the effectiveness of unified modeling, they primarily rely on shared representations or jointly optimized architectures, leaving task-specific communication under heterogeneous supervision less explored. However, detection and captioning differ fundamentally in their supervision granularity, leading to heterogeneous representations with uneven reliability across tasks and spatial locations. In this paper, we reformulate CDC as a selective information routing problem between heterogeneous task branches, where the key challenge is to determine when and where information should be transferred. To this end, we propose CoDeRNet, a unified framework that preserves task-specific representations while enabling bidirectional exchange of complementary information. CoDeRNet introduces a Confidence-Decomposed Routing (CoDeR) mechanism, which decomposes the routing condition into receiver-side need for support and sender-side reliability cues to guide selective transfer. Experiments on LEVIR-MCI and WHU-CDC show that CoDeRNet achieves strong overall CDC performance, particularly improving caption generation while preserving competitive change localization. Extensive analyses further show that the benefits arise not merely from joint learning or generic cross-task communication, but from explicitly modeling selective cross-task information routing. These results highlight the importance of conditional information transfer for unified CDC under heterogeneous supervision.

We study a canonical multi-task demand-learning problem motivated by retail pricing, where a firm seeks to estimate heterogeneous linear price-response functions across multiple decision contexts. Each context is described by rich covariates but exhibits limited price variation, motivating transfer learning across tasks. A central challenge in leveraging cross-task transfer is endogeneity: prices may be arbitrarily correlated with unobserved task-level demand determinants across tasks. We propose a new meta-learning framework that identifies the conditional mean of task-specific causal demand parameters given a subset of task-specific observables despite such confounding, assuming that each task contains at least two distinct locally exogenous price points. This subset is carefully designed to include all of the prices to address cross-task confounding, while masking two demand outcomes that provide randomized supervision to address identifiability issues arising from the inclusion of all prices. We show that this information design is maximally uniformly valid, in that any refinement of the conditioning set that reveals withheld-outcome information is not guaranteed to identify the conditional mean causal target. We validate our method on real and synthetic data, demonstrating improved recovery of demand responses relative to standard transfer-learning baselines.

Source labels tell us where a sample came from, but not how that source oriented its coordinates. In a sparse Gaussian multi-source model with source-specific sign gauges, that distinction changes the learning problem. Without calibration, pooling across sources erases the mean signal, and the source-labelled experiment reduces to sparse PCA. With task-independent relative-gauge metadata, synchronization aligns sources and the same observations become a sparse mean problem. We formalize the value of metadata through aligned mass, which quantifies how much first-order signal a metadata-induced alignment restores. Source labels create only limited aligned mass, whereas valid calibration can create aligned mass proportional to the full sample size. This yields a statistical separation in micro-source regimes and, under standard sparse-PCA hardness assumptions, a computational separation as well. Experiments confirm the predicted phase transition, the failure of invalid metadata, reuse of one calibration graph across many tasks, and controlled real-feature settings with imposed source gauges.

Despite the increasing scale of genome language models (gLMs), their ability to decode the function of regulatory sequences remains unclear. gLM pretraining relies on sequence reconstruction, which may struggle due to the noisy, rapidly evolving nature of regulatory DNA. Self-supervised contrastive approaches provide a promising alternative. Inspired by language-image architectures like CLIP, we demonstrate contrastive promoter-protein pretraining (C3P). By learning to align promoters to their corresponding proteins, we leverage the rich representations of proteins learned by protein language models as supervisory signal for the learning of promoter representations. After training on 88 million bacterial promoter-protein pairs, we evaluate the predictive power of C3P-learned promoter representations for inference of curated regulatory annotations, finding multi-fold improvement over leading gLMs. We also introduce zero-shot co-regulated gene retrieval, the ability to find co-regulated genes in a genome using no experimental data. We find that compared to a randomly initialized baseline, C3P training consistently provides significant zero-shot performance gains, unlike gLMs. Scaling analysis reveals the potential for further improvement as well as the efficiency of C3P, which achieved strong performance at a fraction of the training cost of leading gLMs. In addition to demonstrating that C3P training is effective for learning representations of bacterial regulatory sequences, our strong zero-shot co-regulated gene retrieval performance suggests the possibility of decoding gene regulation for millions of bacteria from their genomes alone.

Brain signals exhibit substantial multi-center heterogeneity, strong inter-subject variability, and highly diverse task settings, which makes it difficult to build models that generalize beyond a single task. While recent brain signal foundation models leverage large-scale pretraining to learn generalizable representations, they often fall short in practice because they still require target-data-specific supervised adaptation, lack multi-task zero-shot capability, and rarely provide verifiable evidence to support their predictions. In this work, we present **BrainCoT**, a multi-task zero-shot Brain signal foundation model with neurometric-anchored Chain-of-Thought (CoT) reasoning. BrainCoT unifies heterogeneous tasks into an instruction-driven framework so that, after pretraining, a single model can be directly applied to multiple tasks in zero-shot settings without additional adaptation. It further introduces neurometric-anchored chain-of-thought grounded in neurometric evidence for verifiable reasoning, and adopts Decision-Consistent Rationale Training to keep process learning aligned with correct predictions. Across extensive evaluations, BrainCoT achieves an average ACC of 67.41\% and AUC of 74.33\% in zero-shot classification without downstream training data, outperforming the strongest linear-probing baseline trained with 1\% labeled downstream data by 3.85\% in ACC and 5.97\% in AUC. Code will be released upon acceptance.

The practical deployment of large language models on resource-limited devices is constrained by three coupled costs: memory footprint, energy consumption, and inference latency. Ultra-low-bit backbones such as BitNet b1.58 mitigate the first two issues by ternarizing the weights to 1.58 bits, but the inference latency remains bounded by autoregressive next-token prediction (NTP), which commits only a single token per forward pass. Multi-token prediction (MTP) improves inference latency by committing several tokens per step. However, the naive combination of MTP with a 1.58-bit backbone does not yield end-to-end gains: once 1.58-bit execution reduces the arithmetic cost, the runtime cost of the accepted-prefix dynamics of MTP dominates each iteration and erodes the savings. In this work, we propose BitMTP, an algorithm--system co-design framework that makes MTP native to a single 1.58-bit backbone. BitMTP is organized around one design principle that we term accepted-prefix alignment: at every iteration, the committed prefix is simultaneously a valid autoregressive continuation, a directly promotable KV slice, and the index of a pre-captured replay state. Three runtime mechanisms enforce this principle along the kernel, memory, and scheduling axes, respectively: a role-width-specialized 1.58-bit execution primitive, a finite-state graph atlas with shared-KV memory over a geometric segment ladder, and an optimistic verify-prefetch scheduler on the verifier-bearing decoding branches. BitMTP-2B reaches 964.2 token/s peak decode throughput, a $2.64\times$ speedup over BitNet b1.58-2B-SFT at comparable prediction accuracy. Code and data will be released.

While LLMs excel at single-turn generation, they struggle with long-horizon, multi-turn interactions. Offline reinforcement learning (RL) offers a scalable approach, yet its performance hinges on the availability and quality of multi-turn trajectory data. A common remedy is to augment training with synthetic trajectories generated by LLMs or simulators, but synthetic data is highly heterogeneous in quality, and naively treating all trajectories as equally informative can degrade performance. We propose BOOST, a bilevel optimization framework where the inner level trains the LLM on reweighted data and the outer level trains a lightweight reweighting head on held-out real validation tasks, assigning continuous trajectory-level weights without requiring an external judge. To ground this approach, we derive a PAC-Bayesian bound revealing a three-way trade-off: synthetic data increases diversity but risks task-shift, while concentrating weight on high-quality trajectories improves empirical performance at the cost of effective sample size. Empirically, our method consistently outperforms multiple baselines. Analysis reveals it upweights synthetic trajectories that align with the real data distribution and exhibit higher qualitative merit.

Many decision-level fusion methods implicitly assume that semantically aligned soft outputs can be directly aggregated once they share the same label simplex. However, this assumption can fail even under identical label semantics, as oracle-specific differences in confidence sharpness, boundary uncertainty, and tail-mass allocation may place soft outputs in incompatible probability geometries and bias direct soft fusion. To address this issue, we formalize this failure mode as geometric incomparability and propose a pre-fusion comparability recovery framework, which learns a shared consensus through constrained oracle-specific rectifiers on the probability simplex. The framework includes a closed-form variant with frozen geometry descriptors and an alternating-refinement variant with consensus-dependent descriptors. Theoretically, we analyze the rectifier family, prove exact recovery under matched radial distortions, characterize the bias of direct fusion, and establish stable recovery under approximate mismatch. Experiments on controlled synthetic stress tests and real black-box decision-level settings show that comparability recovery consistently reduces fusion bias and improves reliability over direct aggregation. These results indicate that geometric comparability is a necessary condition for reliable soft-output fusion. The code is available at \url{https://anonymous.4open.science/r/CFCR-ACR-B8F1}.

Automatic Prompt Optimization (APO) aims to improve the capabilities of Large Language Models (LLMs), yet existing methods remain limited by their result-oriented nature. Training-free approaches often converge to suboptimal prompts, while training-based methods suffer from reward sparsity and optimization instability. More importantly, both paradigms focus on obtaining the final prompt rather than learning the prompt optimization process itself. To address this dilemma, we propose \textbf{ProPO}, a trajectory-driven \textbf{Pro}cess learning framework for \textbf{P}rompt \textbf{O}ptimization guided by multi-dimensional composite rewards. Specifically, ProPO introduces a historical trajectory reflection mechanism for high-quality exploration. Concurrently, we adopt adaptive rubric rewards to provide fine-grained supervision and alleviate reward sparsity. More importantly, we design a novel Process Logic Reward that supervises the consistency of the reflection process. Extensive experiments demonstrate that ProPO, integrated with GRPO, establishes a new SOTA performance. Notably, ProPO achieves a remarkable \textbf{70.00\%} accuracy on the highly complex AIME-2026 task and an absolute gain of \textbf{+25.25\%} over the zero-shot baseline on Subjectivity Classification (Subj). Furthermore, empirical analyses reveal that our multi-dimensional reward mechanism provides finer-grained and denser signals, allowing the model to learn the prompt optimization process, and ultimately enabling it to generate significantly higher-quality prompts.

Early-exit networks promise inference savings by terminating computation at intermediate classifiers, but FLOPs-based gains rarely translate into on-device latency reduction under static-graph compilation. On NVIDIA Jetson Orin Nano, the per-sample exit policy of Shallow-Deep Networks (SDN) runs approximately 2× slower than vanilla ResNet-56 and misses the 60 fps deadline despite using only 40% of the FLOPs, because dynamic branching precludes layer fusion. We propose the Selective Lightweight IC Ensemble Network (SLIENet), a train-full, deploy-partial framework compiled as a single static FP16 engine. SLIENet trains the full backbone with light self-distillation, using the final classifier as a stop-gradient teacher to preserve error diversity across internal classifiers (ICs). At deployment, a sub-second calibration search selects an IC subset for softmax averaging, while overthinking late blocks are truncated at depth k. On CIFAR-100 across ResNet-56, VGG-16, and MobileNetV1, SLIENet outperforms SDN-based variants and remains competitive with ZTW. On Jetson Orin Nano, the recommended SLIE5 configuration improves accuracy by +2.46 pp while reducing p50 latency by 14% and energy by 15% over vanilla, with zero deadline misses across 10,000 test samples. Thus, multi-exit deployment should be a pre-compiled deterministic inference plan, not a sample-wise routing policy.

As language models are increasingly deployed as autonomous agents, they will need to coordinate with others in long-horizon, open-ended interactive tasks. Yet current evaluations rarely test these demands together, focusing instead on short interactions, single-agent open-ended tasks, or highly structured multi-agent settings. We introduce $alem$, a JAX-based, procedurally generated open-ended benchmark for multi-agent coordination built on Craftax-like dynamics. Alem embeds procedurally generated coordination tasks, soft specialisation, communication, and controllable coordination difficulty into a long-horizon survival world with exploration, crafting, trading, and combat. We evaluate $13$ modern LLMs zero-shot within homogeneous teams, with trained MARL agents as reference points. Most LLM agents struggle, averaging only ~6% of maximum reward, although performance varies widely. Gemini-3.1-Pro-High approaches MARL agents trained for one billion steps on Hard coordination ($17.5\%$ vs. $17.6\%$ Coord.\%), Gemma-4-31B-it is the strongest tested open-weight model, and GPT-5.4-High makes strong base progress, while achieving much lower coordination reward. We find that base task competence does not imply coordination competence, communication helps agents share intent, memory and reasoning help with multi-step planning, and heterogeneous teams regress toward average member performance rather than matching their strongest teammate. These results identify coordination as a distinct bottleneck for current LLM agents, separate from single-agent capabilities. Alem makes this bottleneck measurable, providing a controlled setting for developing agents that can communicate, allocate roles, and execute shared plans.

Contextual bandits require balancing nonlinear reward modeling with online efficiency. Tree ensembles capture nonlinearities but require periodic retraining and large replay buffers. Linear models update efficiently per observation with $O(1)$ memory, but are fundamentally restricted to linear reward structures. We propose Residual Quantization (RQ) as a representation layer to bridge this gap. An offline-trained RQ codebook maps continuous contexts into discrete centroid assignments across $\ell$ levels, set dynamically through a shadow mechanism. This enables a spectrum of additive bandit algorithms that achieve nonlinear expressivity with strictly bounded memory. Across 13 datasets, RQ variants beat their non-RQ counterparts on 11 of 13 datasets, often by wide margins, while matching doubling-retrain XGBoost and neural baselines at up to $1000\times$ less memory.

Multi-task learning requires a shared update that makes balanced progress across multiple objectives. A key obstacle is that progress is difficult to compare across tasks with different loss scales, units, and local optimization dynamics. We study this problem from an optimality-gap perspective. For each task, we compare the improvement induced by the shared update with the best one-step improvement achievable by optimizing that task in isolation. This comparison yields a scale-invariant normalized improvement rate, which measures how well the shared update serves each task relative to its own local improvement bound. We formulate balanced multi-task optimization as a max-min problem over the normalized improvement rates, thereby prioritizing the worst-served task. A local quadratic approximation leads to the Karush-Kuhn-Tucker (KKT) optimality conditions, showing that the optimal shared update is determined by a small set of bottleneck tasks. Under an isotropic Hessian approximation, the update admits a closed-form expression that depends only on task gradients and a regularized Gram matrix, while the bottleneck set is identified by an active-set procedure. Experiments on diverse benchmarks show improved task balance and competitive performance.

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.

Understanding how distributed brain regions coordinate during behavior requires models that are both predictive and interpretable. We introduce Behavior-Adaptive Connectivity Estimation (BACE), an end-to-end framework for learning behavior-conditioned directed effective connectivity from multi-region intracranial local field potentials (LFPs). BACE first encodes within-region dynamics with region-specific temporal encoders, then applies a learned adjacency matrix selected for each behavioral context, and finally forecasts future neural activity through a graph-conditioned autoregressive decoder. This design yields explicit region-level connectivity matrices whose edges are tied to predictive dynamics rather than post-hoc explanation. On controlled synthetic time series with known directed graphs, BACE recovers the ground-truth edge structure from forecasting alone. We then evaluate BACE on human deep-brain LFP recordings from three participants performing structured motor tasks. Across participants, BACE achieves strong neural forecasting while producing compact, behavior-specific directed graphs that can be inspected across task segments. Reliability analyses further support the stability of the inferred connectivity patterns. Together, these results position BACE as a practical framework for estimating behavior-adaptive effective connectivity from high-dimensional intracranial recordings, enabling interpretable hypotheses about how deep-brain networks reorganize during behavior.

Recent direct-sum hardness results show that computing multi-head attention by evaluating each head independently is essentially optimal under standard complexity assumptions, but leave open whether the key-value sharing in modern MQA and GQA architectures can circumvent this barrier. We prove that it cannot: even one-layer MQA with a single shared key-value head requires one full quadratic routing computation per query head in the worst case. To quantify how far practice departs from this worst case, we develop a calibration-stacked spectral framework that measures the effective number of independent routing-message directions in a layer, carefully separating signed, stochastic, and softmax-realizable compression; we also prove tight error-compounding bounds and NP-hardness of the natural route-merging compiler problem. Empirically, across fourteen open-weight GQA checkpoints from four model families (0.6B-70B parameters), eight shared routes capture over 90% of the routing-message energy in every model, and larger models use a progressively smaller fraction of their theoretical routing capacity, revealing a substantial and widening gap between worst-case hardness and the redundancy present in deployed transformers.

Watermarking is an important tool for promoting the responsible use of large language models (LLMs). Existing watermarks insert a signal into generated tokens that either flags LLM-generated text (zero-bit watermarking) or encodes more complex messages (multi-bit watermarking). Though a number of recent approaches insert multiple bits into text without perturbing average next-token predictions, they largely extend design principles from the zero-bit setting, such as encoding a single bit per token. In contrast, a watermarker capable of embedding multiple bytes into the text would dramatically increase the potential applications, by embedding information such as the ID of the user who submitted the prompt, the precise model version that was used, or even the prompt itself. We address this problem by introducing ArcMark: a new watermark construction based on coding and information-theoretic principles that is capable of reliably embedding multiple bytes of information into just a few hundred tokens, without any distortion of the underlying LLM next-token distribution. We derive ArcMark by formulating the distortion-free watermarking problem as a channel coding problem, and deriving an information-theoretic channel capacity that establishes the fundamental limit of embedding information in LLM output in a distortion-free manner. This capacity formulation informs the design of ArcMark. In practice, ArcMark outperforms competing multi-bit distortion-free watermarks in terms of reconstruction accuracy, including in the face of attacks that alter a subset of the LLM text. ArcMark output is also shown to be indistinguishable from unwatermarked text in terms of perplexity, and in downstream task quality.

Concurrent multi-agent coding promises division of labor across modules, robustness through redundancy, and parallel exploration at the natural granularity of multi-file projects. Realtime collaborative editing protocols solve this coordination problem for human teams via Conflict-free Replicated Data Types (CRDTs), but the LLMs underneath generate one token at a time and existing multi-agent coding systems inherit this serial limit: they either sequence agents through phase handoffs or pool independent samples without coordination, and a single agent abandons up to 70% of hard tasks with a one-file stub-and-exit. AgentRoom is a realtime collaborative editing protocol for concurrent coding agents: a runtime layer exposing file-level claim, status, and broadcast as MCP tools on top of a CRDT-merged shared filesystem. Across five frontier coding-CLI models on four backend coding tasks, alongside Python DevBench and Rust+axum cross-language checks, AgentRoom ×2 suppresses Solo abandonment on the CLI-stable models and tightens run-to-run variance. Two matched-compute contrasts isolate the cause: parallel-merge vs AgentRoom shows a positive mean LLM-judge contrast, and a bundle probe attributes the gain primarily to the MCP coordination layer rather than to parallelism or substrate convergence alone. Coordination, not parallelism or CRDT-merge, is the load-bearing engineering.

Graph contrastive learning (GCL) learns node and graph representations by contrasting multiple views of the same graph. Existing methods predominantly rely on a small set of fixed, handcrafted views---typically a local and a global perspective---which fundamentally constrains their capacity to capture multi-scale structural patterns. We present an augmentation-free, multi-view GCL framework grounded in fractional-order continuous dynamics. By systematically varying the fractional derivative order $\alpha\in(0,1]$, our encoders produce a continuous spectrum of semantically distinct views: small values of $\alpha$ induce localized, memory-attenuated feature propagation, whereas values approaching $1$ recover broader, global aggregation. Crucially, we treat $\alpha$ as a learnable parameter, enabling the model to automatically identify informative views in a data-driven manner, without resorting to manual view engineering. Extensive experiments on standard node- and graph-level benchmarks demonstrate that the resulting representations are more robust and expressive, consistently outperforming state-of-the-art GCL baselines.