NeurIPS 2026 Papers

A kernel $k(x,y)$ is *LSHable* if there exists a locality sensitive hashing scheme $\mathcal H$ such that $k(x,y)=\Pr_{h\sim\mathcal H}[h(x)=h(y)]$ for all $x,y$. This notion plays a key role in efficient kernel methods in high dimensions. In this work, we show that the $p$-exponential kernel $k(x,y)=\exp(-\lVert x-y \rVert_p)$ is LSHable in bounded regions for all $1<p\leq2$. Previously, this was known only for $p=1$. Our new mosaic LSH scheme is based on a Poisson hyperplane process with hyperplanes sampled as $\ell_1$-biased $p$-stable vectors, for which we develop efficient sampling procedures. As applications, our results yield new and efficient density estimation methods based on LSHability for those $p$-exponential kernels.

Interactive streaming music generation promises the use of generative models for live performance and co-creation that is impossible with offline models. However, SOTA models exist in the discrete-AR regime, requiring industrial levels of compute for both training and inference. In this work, we investigate whether audio diffusion models, with their wide support in the open-source community but non-streaming bidirectional nature, can be repurposed efficiently into interactive models accessible on consumer hardware. By taking a critical look at the modern pipeline for block-wise outpainting diffusion, we identify critical inefficiencies during inference that result in strictly worse computational efficiency than their discrete-AR counterparts. We propose Live Music Diffusion Models (LMDMs), a simple modification of the generative diffusion process that recovers, and then outperforms, the inference complexity of the discrete Live Music Models (LMMs) through block-wise KV Caching. Unlike LMMs, LMDMs further enable stable post-training alignment through our novel ARC-Forcing paradigm, reducing error accumulation without any explicit RL or reward models. We demonstrate the application of LMDMs in a number of creative domains, including text-conditioned generation, sketch-based music synthesis, and jamming. We finally show how LMDMs can be used as a generative instrument in a real artist-AI collaboration, utilizing LMDMs as a “generative delay” to transform musicians’ improvisation live for variable timbral effects while running locally on a consumer gaming laptop.

We introduce LibriBrain100, a large-scale MEG dataset for speech decoding designed from the ground up for reproducible, standardised evaluation. LibriBrain100 more than doubles the size of the original LibriBrain release, resulting in over 100 hours of high-quality MEG acquired while subjects listened to naturalistic continuous speech. With ~80 hours from a single subject, LibriBrain100 sets a new record for ‘deep’, within-subject neural data (8x more than the next comparable dataset and roughly 80x more than other datasets). To demonstrate the payoff of this depth-first design, we evaluate on a word-classification benchmark—an increasingly well-established stepping stone towards the open challenge of non-invasive brain-to-text decoding. Using an existing decoding model, we achieve state-of-the-art performance—validating both the quality of the recordings and the value of within-subject data at scale. Because collecting 80 hours of data per user is impractical for real-world applications, we also collected ~40 minutes of additional data from each of 32 subjects. Using the same word-classification benchmark, we demonstrate the value of ’broad’ multi-subject data: supervised fine-tuning of a pre-trained model can substantially compensate for limited per-subject data. We provide standard train, validation, and test splits, all reproducible through an open-sourced Python library that supports easy downloading, optional preprocessing, and data loading for common deep learning frameworks. In addition, the dataset and evaluation infrastructure are being released alongside an open machine-learning competition with a public leaderboard for standardised benchmarking. Ultimately, our hope is that LibriBrain100 will accelerate progress towards practical non-invasive brain-computer interfaces, capable of restoring communication to people living with severe paralysis.

Large language model (LLM)-based coding agents achieve strong results on controlled benchmarks yet routinely produce pull requests that real maintainers reject. The cause is not functional incorrectness but a lack of organicity: generated code ignores project conventions, duplicates internal APIs, and violates implicit architectural constraints. The latest repository snapshot is insufficient-it reveals the final codebase state, not the change patterns that shaped it. We make three contributions. (1) Paradigm: we propose next-commit prediction as a paradigm for learning agentic coding skill from a repository's own history-each historical commit is a self-supervised target whose oracle diff supplies dense supervision. (2) Evaluation: we establish Organicity Evaluation as a measurable objective for coding agents and contribute the Learning to Commit benchmark-5 curated open-source GitHub repositories under strict per-repository temporal splits-scoring patches along file localisation, internal API reuse, patch bloat, and code-style consistency. (3) Method: we instantiate the paradigm with the Learning to Commit framework, in which the agent performs supervised contrastive reflection: it blindly attempts each historical commit, contrasts its prediction against the oracle, and incrementally distils a reusable skill document that conditions subsequent generations. On the Learning to Commit benchmark and on SWE-bench Pro reframed under our temporal-split protocol, our framework consistently improves organicity on held-out future tasks and further lifts test-pass rates on SWE-bench Pro, narrowing the gap between benchmark success and real-world mergeability.

Data collected from the physical world is always a combination of multiple sources: an underlying signal from the physical process of interest and a signal from measurement-dependent artifacts from the sensor or instrument. This secondary signal acts as a confounding factor, limiting our ability to extract information about the underlying physics. Moreover, it poses significant challenges for combining data in heterogeneous or multi-instrument frameworks. To disentangle these factors of variation, we propose a dual-encoder architecture with a counterfactual generation objective that leverages overlapping observations. The resulting representations explicitly separate intrinsic signals from sensor-specific distortions and noise, and can be used for counterfactual view generation, parameter inference, and instrument-independent similarity search---all unconfounded by measurement artifacts. We demonstrate the effectiveness of our approach in a multi-instrument setting on astrophysical galaxy images from the DESI Legacy Imaging Survey (Legacy) and the Hyper Suprime-Cam (HSC) Survey. This framework provides a general recipe for scientific self-supervised pretraining: construct training pairs from overlapping observations of the same physical system, treat sensor- or modality-specific effects as augmentations, and learn invariant representations through counterfactual generation.

Lipreading remains challenging due to the inherent ambiguity of visual speech cues and their sensitivity to head pose and video resolution. In this work, we propose FLARE, a framework for robust visual speech representation learning via cross-modal distillation and joint face-lip modeling. FLARE leverages semantically rich Whisper representations as the acoustic teacher signal, converting them into discrete distillation targets through a random-projection quantizer and pretraining the model with a masked prediction objective. To capture complementary visual information, FLARE employs a dual-stream architecture that jointly processes lip region-of-interest (ROI) and full-face inputs, where the lip stream captures fine-grained articulatory motion and the face stream encodes broader facial dynamics. During pretraining, auxiliary audio input is incorporated to facilitate cross-modal alignment, and modality dropout is applied to encourage robust visual-only representations. For downstream lipreading, FLARE serves as a visual encoder coupled with either a lightweight Transformer decoder or an LLM-based decoder. Evaluated on LRS2, LRS3, and the out-of-domain WildVSR benchmark, FLARE achieves state-of-the-art word error rates of 11.7%, 13.2%, and 34.3%, respectively. A progressive design study confirms the contribution of each major component. Further analyses demonstrate that joint face-lip modeling improves robustness under degraded video resolution and large head pose variation, while yielding more phoneme-discriminative visual representations. Code and pretrained models are available at https://anonymous.4open.science/r/FLARE-522C.

Retrieval-Augmented Generation (RAG) has become a standard approach for enhancing large language models (LLMs) with external knowledge, mitigating hallucinations, and improving factuality. However, existing systems rely on generating natural language queries at each hop and maintaining a strict architectural separation between retriever and generator, preventing them from leveraging the full representational capacity of the LLM. We propose LAnR (Latent Abstraction for RAG), a unified framework in which a single LLM jointly performs encoding, retrieval, and generation entirely within its own latent space. Rather than generating textual queries, LAnR produces dense retrieval vectors from the hidden states of a designated [PRED] token and uses them to match against encoded document representations from the same model. Furthermore, LAnR adaptively decides when sufficient evidence has been retrieved using a lightweight MLP control head over those same hidden states, eliminating explicit token-level stopping reasoning. Extensive experiments on five QA benchmarks spanning single-hop and multi-hop settings demonstrate that LAnR outperforms existing RAG methods, while achieving improved inference efficiency through reduced number of retrieval calls and tighter model integration.

Progress in Sign Language Translation (SLT) is frequently hampered by the "data bottleneck," a challenge particularly acute for regional languages such as Catalan Sign Language (LSC). In this paper, we present LSC-Parlament, a large-scale, multimodal dataset for LSC derived from Catalan Parliament sessions spanning 2021 to 2024. The dataset comprises over 87 hours of LSC video, synchronized with speech and text translations in either Catalan or Spanish. To curate this resource, we developed a fully automated pipeline that enables alignment between sign language, speech, and text without requiring prior human-annotated data. We provide a systematic evaluation of the dataset across multiple dimensions, including video tracking quality, audio transcription accuracy, and translation benchmarks. Our experiments compare transfer learning, zero-shot, and supervised translation settings, demonstrating significant knowledge transfer between spoken languages in the LSC-Spanish modality. By releasing LSC-Parlament, we provide a challenging open-domain benchmark to foster research in inclusive and scalable translation technologies.

Speech is an increasingly promising biomarker for neurodegenerative disorders due to its non-invasive nature, low cost, and suitability for frequent, longitudinal monitoring. However, existing clinical speech datasets are typically small, sparse, and irregularly sampled, limiting the ability to model continuous disease progression and develop robust biomarkers. We propose L-Flow, a progression-aware conditional speech generation framework for longitudinal speech trajectory completion. Given a patient’s speech recordings, timestamps, and clinical progression labels, L-Flow learns a query-specific progression representation to synthesize speech at intermediate time points within the observed recording span. Experiments on two real-world longitudinal speech datasets demonstrate that L-Flow generates synthetic speech with stronger progression consistency than eight baseline augmentation methods. In addition, incorporating L-Flow–augmented data improves downstream regression performance to predict clinical severity, highlighting its utility for speech-based biomarker development.

Long-term conversational memory poses a fundamental system challenge for LLM agents: reasoning over all past interactions improves coverage but incurs prohibitive token costs and noise, while retrieval-based methods are efficient yet often fail on multi-hop and temporal questions. Existing multi-step retrievers partially address this gap, but typically operate over an ever-growing textual history, causing context expansion and noise accumulation across iterations. We propose MemR$^3$, a backend-agnostic closed-loop controller that reformulates conversational memory retrieval as explicit stateful decision-making. At each iteration, MemR$^3$ maintains an evidence-gap state consisting of grounded information (evidence) and unresolved information requirements (gaps), and uses this state to route among three actions: retrieve, reflect, and answer. Newly retrieved snippets are incorporated into the state and then masked from subsequent prompts, so the model conditions on a compact summary of progress rather than the full retrieval history. This design turns multi-step memory retrieval into a bounded, inspectable control process whose query-time context scales with the state instead of raw accumulated text. Experiments on LongMemEval$_s$ show that MemR$^3$ surpasses implicit multi-step retrieval baselines and often approaches or exceeds full-context prompting while using only 1%-5% of the full-context tokens on long conversations. On LoCoMo, MemR$^3$ consistently improves over its underlying RAG- and Zep-based memory backbones. These results suggest that explicit evidence-gap tracking is an effective abstraction for token-efficient conversational memory.

Self-play fine-tuning enables large language models to improve beyond supervised fine-tuning without additional human annotations by contrasting annotated responses with self-generated ones. Many existing methods rely on a fixed divergence regime. SPIN is closely related to a KL-based regime, SPACE to a Jensen-Shannon-style objective via noise contrastive estimation, and SPIF to $\chi^2$-regularized self-play. Since these divergences exhibit different strengths depending on the distributional gap between model and target, no single choice appears to provide favorable learning dynamics across training stages. We propose IRIS (Interpolative R\'enyi Iterative Self-play), a R\'enyi-based self-play fine-tuning framework with a continuously adjustable objective. IRIS decomposes into two independent tilted risk terms over annotated and synthetic data, with exponential importance weights controlled by the order parameter $\alpha$. We show that several self-play objectives can be interpreted as limiting or representative regimes at particular values of $\alpha$, providing a unified theoretical perspective on these methods. An adaptive order schedule further adjusts $\alpha$ to the distributional gap, shifting from sharper importance weighting early in training to smoother refinement near convergence. Theoretically, we establish the fixed-point property of IRIS and analyze how $\alpha$ controls gradient concentration. Experiments on Zephyr-7B and Qwen2.5-3B across ten benchmarks show that IRIS improves upon baselines, reaching 44.57\% average score with gains across iterations. In our setting, IRIS with only 26$k$ annotated samples surpasses standard supervised fine-tuning trained on the full 200$k$ dataset.

Audio-visual large language models (AV-LLMs) have made strong progress in multimodal understanding, but they typically treat audio as monaural semantic content and therefore struggle to reason about where sounds originate and how they relate to the visual scene. Recent spatial audio-visual studies address parts of this problem, but often focus on specific abilities, such as spatial correspondence or direction and distance reasoning. In this paper, we propose HLR-AVSceneQA, a comprehensive benchmark for spatial audio-visual scene understanding. HLR-AVSceneQA evaluates whether models can jointly hear, localize, and reason by recognizing what is heard, grounding where it comes from in egocentric and allocentric views, and inferring how sound sources relate to visible, hidden, or nearby objects. We further introduce HLR-LLM, which extends a strong audio-visual foundation model with a binaural spatial audio branch. HLR-LLM is trained with a three-stage curriculum, applying chain-of-thought supervision in the final stage to help the model learn structured multi-step spatial reasoning. Experiments show that existing AV-LLMs remain limited in spatial grounding and relational reasoning, while HLR-LLM substantially improves spatial audio-visual scene understanding without sacrificing semantic audio-visual perception.

Large language model (LLM) routing aims to exploit the specialized strengths of different LLMs for diverse tasks. However, existing approaches typically focus only on selecting LLM architectures, treating candidate models as static black boxes while overlooking parameter adaptation, which can substantially affect task performance. In this paper, we introduce HAPS, a hierarchical LLM routing framework that jointly searches over model architectures and parameters. Specifically, a high-level router selects among candidate LLM architectures, while a low-level router generates input-conditioned LoRA parameters for the selected architecture. To couple these two decisions, we design a shared parameter generation mechanism that enables cross-level knowledge transfer between architecture routing and parameter adaptation. We further optimize the whole framework with a reward-augmented training objective. Extensive experiments show that HAPS consistently outperforms strong routing baselines. We further conduct broad ablations and analyses on parameter sharing, architectural choices, scalability, and efficiency, providing additional evidence for the effectiveness and practicality of the proposed framework. We have released our code at https://anonymous.4open.science/r/HAPS_private-68CB.

Spatial and temporal resource constraints are critical for both biological and artificial intelligent systems. Here we define differentiable cost terms for breadth, depth, and time within a recurrent convolutional neural network conceived as a finite subset of an infinite lattice architecture. We optimize these costs jointly with task errors via backpropagation, and efficient computational graphs emerge organically through training. We find that breadth, depth, and time can be traded off against each other to achieve a given level of performance. Networks grow in all three dimensions with task complexity and spontaneously take more recurrent steps when inputs are occluded. Surprisingly, time used by the model correlates with human reaction times in an object recognition task. Our framework provides a normative account of how resource constraints shape neural architectures, connecting to questions about brain design in neuroscience, and may help illuminate the diversity of neural solutions found in nature.

End-to-end speech LLMs often answer the same question less accurately from speech than from text, even when the spoken content is semantically matched to the text prompt. We study this modality gap as a layer-wise inference problem rather than a static embedding mismatch. Across four open-weight speech LLMs on SpeechMMLU and VoiceBench BBH, cross-layer Centered Kernel Alignment (CKA) with speech–text token alignment shows that speech can become geometrically text-like in middle layers while still failing to form stable late-layer answer margins. The central finding is that mid-layer alignment does not imply functional equivalence. ASR-to-LLM controls show that recognition explains part of the gap in some settings but not all of it. Matched-instance margin analysis localizes the failure to weak late-layer answer separation. Attention diagnostics further show that speech evidence remains more diffuse at the decision token. Speech-faithful temporal compaction improves BBH accuracy from 59.9% to 62.1% with tempo 1.2× and to 61.9% with silence trimming, while recovering 34.3% and 38.2% of text-correct/speech-wrong failures. These conditional repairs reveal a 2.35–2.62× asymmetry between temporal compaction and quantity-only KV merging on the same gap subset, motivating post-projection temporal abstraction rather than only input-level feature matching.

Large language models (LLMs) exhibit pronounced position bias in long-context retrieval, systematically prioritizing information location over relevance. Existing mitigations modify positional encodings or attention internals, but in the common output-only deployment setting these are inapplicable. We introduce GOLD PANNING, a Bayesian framework for inference-time active search that (i) reorders documents to concentrate high-belief items in highly diagnostic positions (signal anchoring) and (ii) updates relevance beliefs from model outputs. Unlike active learning, which prioritizes uncertainty reduction, GOLD PANNING exploits anchoring---once flagged, keep it in sight---to preserve weak cues. A greedy assignment derived from the model's diagnosticity profile provably identifies a target among $N$ documents with high probability in $O(\log N)$ rounds. Across open-weight and closed-source models on multi-document QA, GOLD PANNING matches Permutation Self-Consistency's $F_1$ with $30$--$65\%$ fewer queries and remains effective under calibration mismatch, indicating that coarse positional ordering alone drives the gains. These results show that inherent model biases need not be failures, but can serve as exploitable signals.

We present DuplexMotion, a streaming, full-duplex speech-and-motion model designed for dyadic interaction. To capture the continuous and reciprocal nature of human communication, this full-duplex capability empowers the agent to simultaneously perceive and generate both speech and physical motion in a streaming fashion. At its core, our method leverages the strong priors of a foundational full-duplex speech model and integrates a novel motion pathway, thereby achieving fully synchronized multi-modal interaction. Specifically, we design a dual-tower Transformer architecture that preserves the zero-shot conversational reasoning of a frozen base speech model while constructing a deeply coupled, streaming motion pathway. By introducing a unified dyadic token interleaving mechanism and guiding cross-attention via a time-aligned speech-motion RoPE, our model effectively aligns autoregressive motions with rich latent speech features. Trained on the 4,000-hour Seamless Interaction dataset, our model effectively captures cross-speaker dependencies and establishes new state-of-the-art performance across both monadic and dyadic human interaction benchmarks.

LLM-based coding agents are usually evaluated in familiar software settings: mainstream languages, common libraries, and public repositories. These benchmarks remain important, but they can hide how agents behave when the language itself is unfamiliar. We evaluate six contemporary coding agents on four esoteric programming languages using a sequential setup with file editing, local execution, and hidden-test grading. Our protocol exposes capability differences between these agents that mainstream coding and agentic benchmarks such as SWE-Bench Verified and Terminal-Bench 2.0 compress into much narrower bands. We observe that the strongest agents, Claude Opus 4.6 and GPT-5.4 xhigh, often avoid writing the target language directly. On Brainfuck and Befunge-98, they write Python programs that generate target-language code and debug those generators locally. Forbidding this metaprogramming strategy causes large performance drops. Text guidance distilled from this strategy does not materially improve weaker agents. In contrast, Opus-derived Python helper code for building generators, with no solved benchmark programs or hidden-test answers, sharply improves Sonnet 4.6 and GPT-5.4 mini on the same problems, while Haiku 4.5 remains low. More interpreter calls and output tokens improve stronger agents but leave weaker agents near their original performance, indicating that these resources amplify useful strategies rather than create them. Together, these results show that strong coding agents adapt to unfamiliar languages by using tools, feedback, and workspace state to build a working model of the target language. Metaprogramming is the clearest case, but the broader gap is constructing and debugging a strategy that works under the target language's rules.

Multimodal Large Language Models (MLLMs) can listen and see, but how do audio and visual signals actually travel through the network to shape an answer? Despite their growing role in research and real-world applications, the internal pathways through which audio and visual tokens influence the final prediction remain poorly understood. In this study, we examine audio-visual information flow inside Audio-Visual Large Language Models (AVLLMs), tracing how AVLLMs route, utilize, and integrate audio and visual information across two input configurations, audio-visual video and multiple interleaved audio-visual items. We find that for audio-visual video, AVLLMs follow the sequential information flow pathway established for VLMs and VideoLLMs, with audio and visual contribution flowing along this pathway in proportion to the task's reliance on each modality. In settings with multiple interleaved audio-visual items, this routing shifts to different parallel streams. Furthermore, we demonstrate that audio-visual and other token types can be discarded once their information is transferred to LLM, with minimal impact on the model's prediction or even slight improvement, generalizing across multiple tasks and datasets, enabling more efficient inference. These findings hold across multiple models and scales, Qwen2.5-Omni and Video-SALMONN2 Plus at 3B and 7B scales, leading to hypotheses on why these flow structures emerge. Together, these results deliver the first coherent picture of how AVLLMs orchestrate sound and sight inside the network and lay the groundwork for the next wave of interpretability, design, and efficiency advances in audio-visual and broader MLLMs.