NeurIPS 2026 Papers

Diffusion-based super-resolution (SR) is a key component in video generation and video restoration, but is slow and expensive, limiting scalability to higher resolutions and longer videos. Our key insight is that many regions in video are inherently low-detail and gain little from refinement, yet current methods process all pixels uniformly. To take advantage of this, we propose SkipSR, a simple framework for accelerating video SR by identifying low-detail regions directly from low-resolution input, then skipping computation on them entirely, only super-resolving the areas that require refinement. This simple yet effective strategy preserves perceptual quality in both standard and one-step diffusion SR models while significantly reducing computation. In standard SR benchmarks, our method achieves up to 60$\%$ faster end-to-end latency than prior models on 720p videos with no perceptible loss in quality.

Agent Skills are structured packages of procedural knowledge that augment LLM agents at inference time. Despite rapid adoption, there is no standard way to measure whether they actually help. We present SkillsBench, a paired benchmark of 84 tasks across 8 expertise-heavy domains; every task runs under three matched conditions (no Skills, curated Skills, self-generated Skills) on 7 agent--model configurations and 7,326 trajectories with deterministic verifiers. Curated Skills raise pass rate by 16.4\,pp on average, but the lift is heterogeneous: per-domain $\Delta$ spans +7.2 to +24.8pp and 18 of 84 tasks regress. Self-generated Skills net -1.3pp, indicating that current models cannot author the procedural knowledge they benefit from consuming. SkillsBench establishes paired evaluation as standard practice for measuring agent augmentations.

Memory-augmented reinforcement learning strengthens LLM agents' ability to solve complex long-horizon tasks. Skills are one such form of memory, pairing instructions with an applicability condition over task types. However, retaining every skill indiscriminately as the policy improves lets obsolete or harmful entries accumulate and mislead the agent. We propose SkillForge, an agentic RL method that compiles and evolves the skill library through a fitness-driven skill lifecycle of trial, active, stable, and retired states, so that the skills and the model co-evolve throughout training. A preliminary evaluation phase first uses the base model's own rollouts to pre-retire low-fitness skills, yielding a filtered library that then seeds supervised fine-tuning. Reinforcement learning takes over from this checkpoint, and at each iteration selective retirement, stabilization, and LLM-guided mutation continue to forge the skill library alongside policy optimization. Across multiple interactive agent benchmarks, SkillForge achieves the highest aggregate success rate, delivering up to 7.8\% relative improvement over the strongest baseline while keeping the skill library compact throughout training. We introduce Skillfurnace, a dataset of 5k+ annotated records bundling retirement-filtered SFT trajectories, evolved skill libraries with fitness annotations, and retirement events with human-annotated failure categories to support research on skill quality and lifecycle management.

Face anti-spoofing (FAS) is critical for protecting face recognition systems against presentation attacks in open environments. Beyond high cross-domain generalization, practical FAS deployments must provide interpretable and uncertainty-aware decisions when multimodal evidence is incomplete, degraded, or conflicting. However, current multimodal domain-generalized FAS methods focus primarily on feature fusion or domain alignment, compressing RGB, depth, and infrared cues into opaque logits. This makes it difficult to discern which modality drives the decision, whether modalities reinforce or suppress each other, and whether a given sample lies within the classifier's reliable operating regime. We introduce SEM-FAS, a quantum-inspired yet fully classical single-pass evidence measurement framework that jointly enhances generalization, interpretability, and reliability. SEM-FAS encodes each modality as a normalized complex-valued state whose amplitude captures evidence strength and whose phase encodes cross-modal interference; multiple coherent heads are then mixed into a density-matrix representation. A constrained POVM-like measurement head converts this representation into calibrated class probabilities while analytically decomposing every prediction into per-modality main effects and pairwise synergy/suppression terms. Simultaneously, three built-in uncertainty indicators are obtained without sampling overhead: predictive ambiguity, representation mixedness, and measurement-coverage mismatch. On standard multimodal DG-FAS benchmarks, SEM-FAS reduces the best baseline HTER by 2.18\% and improves AUC by 1.38\% under both complete and missing-modality protocols. Visual analyses further suggest that the learned modality effects, pairwise interactions, and uncertainty scores align with expected FAS behavior, even for hard samples, indicating that single-pass evidence measurement is a promising way to jointly enhance generalization, interpretability, and reliability in multimodal FAS.

The de facto standard way of training neural networks requires storing activations at each layer to memory, in order to compute gradients in backpropagation. As progress has often been made by stacking larger and more layers, memory consumption has now become a major bottleneck for making frontier models available to everyone. In this work, we take advantage of the Residual Network (ResNet) layer formulation that is now used in many state-of-the-art neural network architectures to recompute the activations at each layer on-the-fly during backpropagation instead of storing them in memory. We propose a modification to Reversible Residual Network (RevNet) that overcomes its limitation of requiring to replace the ResNet layer network function with two new network functions, by setting either one to an identity function. This modification is simple yet has a significant advantage because now there is no need to change the original ResNet architecture. Our method can work as a drop-in replacement for layers with residual connections such as in ordinary ResNets or Transformers with practically no loss in performance while offering significant reductions in memory consumption. With this simple modification, we are able to maintain the same computational cost and ease of use as activation checkpointing, which requires no architectural changes, while leveraging a reversible procedure such that we only need to store the activations from the last layer. The method is called Simplified RevNet, and compared to previous work in reversible architectures, we here propose a simpler and more streamlined approach that comes in two variants based on which of $F$ or $G$ in RevNet becomes an identity function. Empirically, we demonstrate performance at practically the same level as the non-reversible counterparts on ImageNet image classification with ResNets and on OpenWebText language modeling with Transformers.

Code evolution is a family of techniques that rely on large language models to search through possible computer programs by evolving existing code. While code evolution pipelines have shown impressive performance across domains, many are highly complex and are typically not compared to simpler alternatives. To prevent bad comparisons from stagnating the field, we propose two simple baselines and compare them to popular pipelines across three domains: finding better mathematical bounds, designing agentic scaffolds, and machine learning competitions. Surprisingly, the baselines match or outperform existing methods across all domains, prompting a closer look at which factors drive performance in different settings. For mathematical bounds, we find that the search space matters far more than the exact search algorithm, with both simple and complex methods performing similarly given the same search space. Thus, expanding the search space is what matters for improving performance, and does so across all pipelines. Moreover, different domain knowledge embedded in prompts can significantly affect the search's efficiency, potentially leading to overestimating a pipeline's sample efficiency. For automated agentic scaffold design, we show that typical high-variance evaluations lead to all automated search methods picking subpar scaffolds relative to a simple majority vote. To mitigate this, we propose improved evaluation procedures that reduce stochasticity while keeping the search economically feasible. Overall, these results indicate that further improving code evolution's performance depends more on the setup surrounding the search pipeline than on the pipeline itself. We hope these insights will enable developing new code evolution methods and help spur meaningful progress in the field.

Diffusion large language models (dLLMs) have recently emerged as a promising alternative to autoregressive (AR) LLMs, offering faster inference through parallel or blockwise decoding. However, their masked language modeling formulation remains incompatible with standard token-level speculative decoding, one of the most effective acceleration techniques for AR models. In AR decoding, the causal mask preserves temporally valid token-level contexts, enabling a target model to verify multiple drafted tokens in a single forward pass. In contrast, dLLMs rely on mask tokens and bidirectional attention, causing the effective context to change across denoising steps and preventing direct token-level speculative verification. To bridge this gap, we propose a simple but effective speculative decoding algorithm for diffusion language models, named SimSD, which mainly adopts a plug-and-play masking strategy that equips dLLMs with temporally valid token-level contexts for speculative decoding. Our method explicitly introduces reference tokens from draft-model predictions and designs an attention mask that regulates their interaction with current-step tokens, allowing dLLMs to compute valid logits for drafted tokens in a single forward pass. This restores the key verification ability provided by causal masking in AR models while preserving the parallel decoding advantages of dLLMs. The proposed method is training-free and can be flexibly integrated with other acceleration techniques such as KV cache and blockwise decoding. Experiments on SDAR-family dLLMs across four benchmarks show that our method achieves up to \(7.46\times\) higher decoding throughput while maintaining and even improving average generation quality. Our code will be publicly released.

We investigate the short-context dominance hypothesis: that for most sequences, a small local prefix suffices to predict their next tokens. Using large language models as statistical oracles, we measure the minimum context length (MCL) needed to reproduce accurate full-context predictions across datasets with sequences of varying lengths. For sequences with 1–7k tokens from long-context documents, we consistently find that 75–80\% require only the last 96 tokens at most. Given the dominance of short-context tokens, we then ask whether it is possible to detect challenging long-context sequences for which a short local prefix does not suffice for prediction. We introduce a practical proxy to MCL, called Distributionally Aware MCL (DaMCL), that does not require knowledge of the actual next-token and is compatible with sampling strategies beyond greedy decoding. Our experiments validate that simple thresholding of the metric defining DaMCL achieves high performance in detecting long vs. short context sequences. Finally, to counter the bias that short-context dominance induces in LLM output distributions, we develop an intuitive decoding algorithm that leverages our detector to identify and boost tokens that are long-range-relevant. Across Q\&A tasks and model architectures, we confirm that mitigating the bias improves performance.

As LLM-based agentic systems grow more complex, they increasingly rely on meta-agents: higher-order agents that act on other agents, much like managers supervise employees. Yet existing agentic runtimes expose execution only as static environmental states, limiting the kinds of live and post-hoc interventions a meta-agent requires. To unlock these capabilities, we introduce Shepherd, a functional programming model that formalizes meta-agent operations on target agents as functions, with core operations mechanized in Lean. Shepherd records every agent–environment interaction as a typed event in a principled Git-like execution trace where any past state can be cheaply forked and replayed. Even at scale, Shepherd forks the agent process and its filesystem 5x faster than Docker, with >95% prompt-cache reuse on replay. We exemplify Shepherd's versatility in three use cases: (1) runtime intervention, where a live supervisor improves pair coding pass rate from 28.8% to 54.7% on CooperBench; (2) counterfactual meta-optimization, where a meta-agent branches to explore alternative paths, beating MetaHarness and GEPA across four benchmarks by up to 11 points with up to 58% lower wall-clock; and (3) Tree-RL training, where a meta-agent forks rollouts at chosen turns, lifting TerminalBench-2 from 34.2% to 39.4% on Qwen3.5-35B-A3B. These use cases show Shepherd is a performant and efficient substrate for programming meta-agents; we open-source it to advance future research.

Predicting the maximum stable learning rate of a neural network from its architecture alone has remained outside the reach of current theory, except for deep linear and shallow scalar models, where exact sharpness expressions are available. We close this gap for deep polynomial networks by proving the first computable, architecture-explicit lower bound on the largest Hessian eigenvalue at any interpolating minimum. The bound factorizes into three independently computable terms: a data-geometry factor, an architectural-capacity factor, and a label-energy factor. Combining our bound with the dynamical framework of Chemnitz and Engel [2025] and the stability conditions of Cohen et al. [2022], we derive critical step-size upper bounds for a range of optimizers from vanilla gradient descent to adaptive versions like Adam. The maximum stable learning rate for deep polynomial networks scales as $(\text{activation degree})^{(-2 \times \text{depth})}$, placing realistic polynomial network training in the Edge of Stability regime. Our derivation shows that this scaling is a Hessian-level consequence of the network's algebraic structure, not a property of any specific optimizer or dataset. With identity activation, our bound recovers the exact deep linear sharpness of Mulayoff and Michaeli [2020].

Origin-destination (OD) flow forecasting supports decisions about where and how many people move through a city. Most existing public OD benchmarks provide only a partial view of citywide mobility, covering a single transport service or at most two modes. With partial mode coverage, observed demand changes are difficult to separate into shifts across transport modes and changes in total travel. We introduce SeoulMMOD, a large-scale public benchmark for citywide multimodal mobility in Seoul. SeoulMMOD provides three years of hourly OD flows estimating total mobility for 6 urban travel modes across 25 districts and 426 sub-districts, together with travel-time, travel-distance, calendar, rainfall, and point-of-interest (POI) signals. Using SeoulMMOD, we benchmark 14 forecasting baselines under a common protocol and study spatial scaling, joint training versus training one model per mode, and cross-year generalization. Results show that several spatio-temporal models that work at district level do not scale to sub-district OD forecasting, and that naive joint multi-mode training does not consistently improve accuracy. Together, the dataset and benchmark establish a reproducible testbed for city-scale multi-mode OD forecasting across multiple years. Project page: https://anonymous.4open.science/r/SeoulMMOD

Emergent misalignment (EM) has been linked to the activation of misaligned persona vectors and evil character traits, suggesting that EM operates through disruption of the model's aligned character rather than direct learning of harmful content. Motivated by this connection, we study self-generated text recognition (SGTR) finetuning as a character-targeted intervention that is distinct from existing in-training defenses. We conduct two-stage finetuning experiments across three models (GPT-4.1, Qwen2.5-32B-Instruct, Seed-OSS-36B-Instruct) and multiple EM datasets to compare SGTR finetuning against benign finetuning baselines (correct domain-specific data, general knowledge, and word counting) to find it an effective defense in both reversal and prevention settings. We find that all interventions produce comparable EM reversal, but only when restoring capabilities that EM had degraded. For prevention, only SGTR finetuning consistently reduces misalignment without exacerbating any individual metric, suggesting that character fortification specifically drives prevention. We provide further evidence for EM's relation to the LLM's default character by showing that EM finetuning induces diversity into the LLM's identity self-reports, artificially corrupting self-recognition exacerbates misalignment caused by EM finetuning, and that removing the model's identity-bearing system prompt substantially reduces the effect of EM finetuning. Together, these findings reframe EM not as the adoption of a coherent misaligned persona but as the destabilization of aligned character.

Developing self-evolving agents that learn continuously from streams of information represents an emerging frontier in artificial intelligence. This capability is critical for efficiently updating computationally expensive systems such as foundation models. When agents learn by exploring the environment and interacting with other agents to achieve multiple tasks over time, two sources of non-stationarity emerge: shifting task distributions and co-evolving peers. However, developing frameworks for continual multi-agent reinforcement learning (CMARL) and effective policy improvement remains challenging. In this paper, we argue that training self-evolving agents should incorporate two classes of model-based principles: internal models that allow an agent to compare policy checkpoints, monitor its learning capacity, and represent its current objective; and external models that learn environment dynamics and the behavior of other agents. We anticipate that discussion on these principles will facilitate the design of algorithms, architectures, and benchmarks that explicitly evaluate model-based components in CMARL.

The Forward-Forward (FF) algorithm trains networks layer-by-layer using a local "goodness function," yet the principles governing what makes an effective goodness function remain largely unexplored. We systematically explore the goodness-function design space and identify a unifying principle: the goodness function must be sensitive to the shape of neural activity, not its total energy. This principle is motivated by the observation that deep network activations follow heavy-tailed distributions and that discriminative information is often concentrated in peak activities. We propose two complementary families: selective functions (top-k, entmax-weighted energy) that measure only peak activity, and shape-sensitive functions (excess kurtosis / "burstiness" and higher-order moments) that reward heavy-tailed distributions via scale-invariant statistics. Combined with separate label-feature forwarding (FFCL), controlled experiments across 13 goodness functions, 5 activations, 6 datasets, and three continuous sweeps each tracing a characteristic inverted-U yield 89.0% on Fashion-MNIST and 98.2±0.1% on MNIST (4x2000)—a +32.6pp gain over SoS—with consistent improvements across all benchmarks (+72pp USPS, +52pp SVHN). The scale-invariant nature of burstiness makes it particularly robust to magnitude shifts across layers and datasets. Code is available at [https://anonymous.4open.science/r/ff-selectivity-shape](https://anonymous.4open.science/r/ff-selectivity-shape).

Cross-view localization classically asks: where does this ground image lie on the satellite tile? Existing methods are typically limited to 3-DoF estimates---an $(x,y)$ position and a yaw angle---because nadir satellite imagery provides no direct cues for roll, pitch, or altitude, forcing a reliance on planar-motion and zero-tilt assumptions. These assumptions break on real terrain with slopes, ramps, and tilted camera mounts. To overcome this, we introduce a single UAV image as an intermediate viewpoint: it reveals the 3D structure invisible from nadir, supplies the cues for roll, pitch, and altitude that the satellite alone cannot provide, and needs only spatial overlap with the ground camera---no known relative pose is required. Building on this insight, we propose **Cross3R**, a flexible feed-forward model that ingests a satellite tile together with a UAV image, a ground image, or both, and, in a single forward pass, recovers a cross-view 3D point cloud, the 6-DoF poses of every input camera, and the on-tile $(x,y)$ position and yaw of each perspective camera. For training and evaluation, we also construct **CrossGeo**, a 278K-image tri-view dataset spanning 85 scenes across every continent except Antarctica. On CrossGeo, Cross3R consistently outperforms feed-forward 3D baselines in point-cloud reconstruction, 6-DoF camera-pose estimation, and cross-view localization. On KITTI, it outperforms dedicated cross-view methods trained on KITTI on most metrics, despite having no KITTI training itself.

Producing output that conforms to a specified JSON schema underlies tool use, structured extraction, and knowledge base construction in modern large language models. Despite this centrality, public datasets for the task remain small, synthetic, or text-only, and rarely pair real page content with the prompts and schemas used in practice. We introduce ScrapeGraphAI-100k, 93{,}695 schema-constrained extraction events collected via opt-in ScrapeGraphAI telemetry in Q2--Q3 2025, deduplicated and balanced by schema from 9M raw events. The corpus spans 18{,}000+ unique schemas across 15 named languages plus a long-tail Other category, with English and Traditional Chinese covering 88\% of detected content, each instance pairs Markdown-converted page content with a prompt, schema, LLM response, and per-example jsonschema-rs structural conformance labels (semantic correctness is out of scope, and raw HTML is deferred beyond v1.0). We characterize structural diversity across the corpus and identify sharp failure thresholds as schema complexity grows. As a case study, a 1.7B student fine-tuned on this data closely tracks the output distribution of its GPT-5-nano teacher, though it still trails a 30B-A3B reference (3.3B active parameters) on schema compliance. We offer this distillation result as preliminary evidence that grounding schema-constrained generation in real practitioner workloads at scale enables training and benchmarking that prior synthetic or text-only corpora could not support.

Large language models trained on unrestricted internet corpora inevitably embed information from the future, introducing lookahead bias that compromises the validity of backtests and causal inference in finance and the social sciences. Point-in-time language models—trained exclusively on text available up to each calendar date—eliminate this leakage by construction, but existing efforts typically produce models that lag substantially behind their unconstrained counterparts. We show that this performance gap can be narrowed through scale. Training decoder-only transformers with up to 4 billion parameters on 1 trillion chronologically filtered tokens from FineWeb, we construct a sequence of yearly model checkpoints spanning 2013–2024. Across a range of common-sense reasoning and language understanding benchmarks, our models approach the performance of leading open-weight models of comparable size (such as Gemma-3-4B and LLaMA-7B) trained on temporally unrestricted data, although a performance gap remains on several tasks. Finally, in a strict out-of-sample economic evaluation task, portfolios built from point-in-time embeddings achieve robust positive Sharpe ratios and perform close to full-sample counterparts that violate temporal validity, indicating that chronologically consistent language models can extract economically meaningful signals without relying on look-ahead bias. We release the complete pipeline—including dataset construction, training infrastructure, and evaluation code—to enable reproducible point-in-time language modeling and to support research applications that require strict temporal validity. Code is available publicly on GitHub (https://github.com/DjoFE2021/Point-In-Time-LLM) and model checkpoints as well as training datasets can be accessed on Hugging Face (https://huggingface.co/datasets/Diamegs).

Progress in genomic language modeling depends on pretraining resources that are both diverse and comprehensive, as well as benchmarks that evaluate models across biological scales from genes to whole genomes. This need is especially acute in bacteria, which are extremely abundant, diverse, and central to human health, ecology, and biotechnology. Yet resources for large-scale pretraining and evaluation in bacterial genomics remain limited, with existing frameworks focusing largely on eukaryotic settings. Here, we introduce BacCorpus, a genome-level resource for training genomic LMs comprising 20B proteins, including 4.6B unique sequences, from 7M genomes spanning more than 150,000 species, and BacBench, a comprehensive benchmark spanning 10 datasets and 5 tasks across gene-, system-, and genome-level tasks. We evaluate a diverse suite of genomic LMs on BacBench and additionally develop BacLM, a 350M-parameter model trained jointly on protein sequences and intergenic DNA to further investigate how bacterial genomes are best represented. Across tasks, we find that performance depends strongly on biological scale: methods leveraging protein sequence are competitive on gene-level prediction, whereas models that incorporate wider genomic context excel at problems which involve complex multi-gene interactions. Together, BacCorpus and BacBench provide a practical foundation for training and evaluating robust, genome-aware genomic language models in bacteria at an unprecedented scale.

Training neural networks at scale requires careful per-layer scaling of initialization, learning rate, and other optimizer hyperparameters, with the right rules depending jointly on the architecture, the optimizer, and which axis (width $D$, depth $L$, batch size $B$, etc.) is being scaled. Current practice is to derive each rule by hand, as in $\mu$P for width, CompleteP for depth, SDE-based and EMA-timescale arguments for batch size, and tailored prescriptions for matrix-preconditioned optimizers; as architectures, optimizers, and scaling axes continue to evolve, this hand derivation is increasingly a bottleneck to combining these advances at scale. We show that these derivations can be systematized by solving a linear system over hyperparameter exponents to satisfy the scaling constraints imposed by each primitive instruction of the training program, analogous to dimensional analysis in physics that enforces matched units on both sides of an equation. We develop \emph{automatic parameterization} (AutoP), a system that reads these rules off a traced training program and solves the resulting system to assign a scaling exponent to every hyperparameter, and provide a concrete JAX implementation. From a few primitive rules, AutoP recovers $\mu$P for width on Transformers, Monarch-structured Transformers, mixture-of-experts, and ResNets under Adam, SignSGD, Muon, and AdaMuon; CompleteP for depth; and the known batch-size prescriptions for the learning rate and AdamW weight decay. It also identifies new rules for the recurrence count in looped transformers, the context length in linear-attention and MLP-Mixer models, and the batch-size scaling for Muon's learning rate, all of which we demonstrate as empirically beneficial. Analogous to automatic differentiation, our results suggest that robust hyperparameter scaling rules can be automated, freeing practitioners to scale up new architectures and optimizers without rederiving the underlying theory or risking subtle errors that quietly cost compute at scale.