NeurIPS 2026 Papers

Many scientific and combinatorial problems admit multiple correct solutions, not a single label. Standard supervised learning resolves this ambiguity by choosing one solution as the target, but this hidden selector can be arbitrary, discontinuous, and harder to learn than the underlying solution set. We study bifurcation models, a weight-tied dynamical view in which different initializations can converge to different stable equilibria, so the model represents an attractor landscape rather than one chosen branch. We prove that broad set-valued maps with locally Lipschitz branches can be represented by regular equilibrium dynamics and that the induced selectors are almost everywhere regular, while manual selectors can be arbitrarily irregular. Experiments on frustrated Ising models show that such dynamics can discover multiple valid equilibria without branch labels and outperform single-branch supervision. Allen-Cahn experiments further show that diversity is not automatic: it can be encouraged explicitly, but with an accuracy--diversity tradeoff.

Scientific research proceeds through iterative cycles of hypothesis generation, experiment design, execution, and revision, often requiring researchers to explore multiple competing directions as evidence accumulates and priorities shift. LLM agents can automate parts of this process, but existing agents either concentrate reasoning within a single research thread or coordinate through a central planner with fixed objectives. As a result, they struggle to sustain parallel exploration across research directions or reorganize as promising and unproductive directions emerge over time. We introduce AutoScientists, a decentralized team of AI agents for long-running computational scientific experimentation. Rather than following decisions from a central orchestrator, agents independently interpret a shared experimental state, self-organize into teams around research directions, critique and filter proposals with a discussion phase before committing experimental compute, and exchange both successful and failed findings across teams to avoid redundant exploration. Under matched experimental budgets, AutoScientists outperforms prior agentic systems across biomedical machine learning, language-model training optimization, and protein fitness prediction. On BioML-Bench, spanning biomedical imaging, protein engineering, single-cell omics, and drug discovery, AutoScientists achieves a mean leaderboard percentile of 74.4% across 24 tasks, improving over the strongest prior biomedical agent by +8.33%. On GPT training optimization, AutoScientists reaches a target validation bits-per-byte 1.9x faster than autoresearch and continues discovering improvements from a stronger starting champion where the single-agent approach finds none (7 vs. 0 accepted improvements). On ProteinGym fitness prediction, AutoScientists discovers a method for ACE2-spike binding that improves over current state-of-the-art model by +12.5% Spearman correlation. Applied without modification to all 217 ProteinGym assays, the same method improves over the prior state of the art by +6.5% in Spearman correlation. Code: https://anonymous.4open.science/r/autoscientists_anonymize-D10C

LLM-based peer review is now used at production scale, but rigorous evaluation remains limited. We introduce a benchmark for evaluating AI peer reviewers’ detection of injected errors and quantifying false-positive rates across error type, severity, prompting strategy, and reviewer architecture. We also present skepthical, a multi-agent AI reviewer that verifies citations by retrieving and reading cited papers, checks mathematics with computer algebra, audits numerical claims with code, and uses a cross-model ensemble for general scientific issues. On 15 astrophysics preprints containing 177 injected errors, we benchmark 12 reviewer systems: skepthical; six single-LLM reviewers from three providers, each run with free-form and structured prompts; two Claude Code reviewers using the same Claude model and tools, one with a free-form prompt and one with a structured prompt; and three external reviewers. Detection performance varies sharply by model, prompt, tool use, and error type. Under the same free-form prompt, three frontier LLMs differ by 48.6 pp in total detection rate; a structured prompt halves this spread by improving the weaker models. Within Claude Code, changing only the prompt from free-form to structured raises detection by 32.2 pp. skepthical achieves the highest overall detection rate, 70.6±6.2%, with the best performance on general issues, 82.2%, and citation errors, 46.7%. On numerical errors, it matches both GPT-5.5 configurations at 80.0%, behind structured Claude Code at 91.1%; on mathematics, its 73.8% rate overlaps with both GPT-5.5 configurations within confidence intervals. False-positive rates also vary substantially: free-form Opus 4.7 has the lowest overall rate at 1.0%, while skepthical is the only reviewer with no false-positive failure mode jointly across mathematics, numerics, and citations, yielding a 2.8% overall rate. skepthical is deployed publicly at skepthical-ai.org, and we release the evaluation set as a benchmark.

Despite being a pivotal frontier, interactive world modeling remains underexplored in terms of the versatile controllability required by practical scenarios. To bridge this gap, we present AnchorWorld, a framework that advances egocentric simulation through enhanced interaction integrity and a flexible mechanism for world customization. First, we utilize 3D human motion as the primary interaction modality. To complement the out-of-view or truncated body parts in egocentric views, we introduce an auxiliary training supervision that incorporates exogenous viewpoints decoupled from the agent’s first-person sensorium. It allows the model to observe the agent's full-body positioning relative to the environment, facilitating a more robust spatial grounding of human-world interactions. Furthermore, we propose a simple yet effective mechanism for customizing self-evolving worlds. This is achieved by defining anchor views within a unified world coordinate system, coupled with textual descriptions dictating the dynamic evolution of local scenes. Experimental results show that AnchorWorld significantly outperforms state-of-the-art baselines, while ablation studies validate the effectiveness of our key designs. Notably, our customization scheme exhibits promising spatio-temporal geometric consistency and adheres strictly to the prescribed evolutionary dynamics.