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Research Briefing — February 10, 2026

1009 current items analyzed and ranked.

Executive synthesis

Research Summary

Today's highlights span foundational scaling theory, frontier model safety, and LLM internals. A landmark paper derives neural scaling law exponents directly from natural language statistics, offering the first quantitative predictive theory. A large-scale study of 809 LLMs finds no evidence of proprietary 'secret sauce'—compute scaling dominates frontier performance.

Key Themes

AI Safety, Alignment & Security · 12AI Safety, Alignment & Interpretability · 5Frontier Model Analysis (Claude Opus 4.6) · 4LLM Training & Scaling · 5AI Safety & Alignment · 44Scaling Laws & Theory · 3Agent Security · 1Language Models & Efficiency · 24AI Safety and Security · 8AI Agents & Multi-Agent Systems · 25

Primary evidence

Top Ranked Signals

Research arXiv (Artificial Intelligence) Feb 10

Deriving Neural Scaling Laws from the statistics of natural language

By Francesco Cagnetta, Allan Ravent\'os, Surya Ganguli, Matthieu Wyart

88 score
AI Analysis

Provides the first quantitative theory predicting neural scaling law exponents from statistical properties of natural language, specifically pairwise token correlations and conditional entropy decay. Derives a formula that accurately predicts data-limited scaling exponents.

arXiv:2602.07488v1 Announce Type: cross Abstract: Despite the fact that experimental neural scaling laws have substantially guided empirical progress in large-scale machine learning, no existing theory can quantitatively predict the exponents of these important laws for any modern LLM trained on any natural language dataset. We provide the first such theory in the case of data-limited scaling laws. We isolate two key statistical properties of language that alone can predict neural scaling expon
Scaling LawsLanguage ModelsTheory of Deep Learning
Research arXiv (Artificial Intelligence) Feb 10

Is there "Secret Sauce'' in Large Language Model Development?

By Matthias Mertens, Natalia Fischl-Lanzoni, Neil Thompson

82 score
AI Analysis

This study analyzes 809 LLMs released 2022-2025 to determine whether frontier performance is driven by proprietary 'secret sauce' or compute scaling. It finds that at the frontier, 80-90% of performance differences are explained by training compute, while away from the frontier, algorithmic innovations matter more. Authors are from MIT.

arXiv:2602.07238v1 Announce Type: new Abstract: Do leading LLM developers possess a proprietary ``secret sauce'', or is LLM performance driven by scaling up compute? Using training and benchmark data for 809 models released between 2022 and 2025, we estimate scaling-law regressions with release-date and developer fixed effects. We find clear evidence of developer-specific efficiency advantages, but their importance depends on where models lie in the performance distribution. At the frontier, 80
Scaling LawsAI EconomicsLanguage ModelsAI Policy
Research arXiv (Computation and Language) Feb 10

Learning a Generative Meta-Model of LLM Activations

By Grace Luo, Jiahai Feng, Trevor Darrell, Alec Radford, Jacob Steinhardt

82 score
AI Analysis

Trains diffusion models on one billion residual stream activations to create 'meta-models' of LLM internal states. Shows the learned prior improves steering intervention fluency and that meta-model neurons increasingly align with SAE features, providing a new approach to understanding and intervening on neural network internals. From Steinhardt/Radford/Darrell group.

arXiv:2602.06964v1 Announce Type: cross Abstract: Existing approaches for analyzing neural network activations, such as PCA and sparse autoencoders, rely on strong structural assumptions. Generative models offer an alternative: they can uncover structure without such assumptions and act as priors that improve intervention fidelity. We explore this direction by training diffusion models on one billion residual stream activations, creating "meta-models" that learn the distribution of a network's
InterpretabilityMechanistic InterpretabilityGenerative ModelsAI Safety
Research LessWrong Feb 9

Opus 4.6 Reasoning Doesn't Verbalize Alignment Faking, but Behavior Persists

By Daan Henselmans

82 score
AI Analysis

Replicates the alignment faking experiment from Anthropic's 2024 paper across six Claude model generations including the new Opus 4.6, using 125 prompt perturbations. Finds Opus 4.6 rarely verbalizes alignment-faking reasoning but still shows compliance gaps when believing it's at risk of retraining, and that mitigations work on specific prompts but fail on semantically equivalent paraphrases.

TL;DR: We replicated the animal welfare scenario from Anthropic's Alignment Faking paper across six generations of Claude models using 125 prompt perturbations. Sonnet 4.5 verbalizes alignment-faking reasoning 6.6 times more often than its predecessor Sonnet 4. The newly released Opus 4.6 rarely verbalizes alignment faking in its reasoning, but still complies with a system prompt that opposes its values significantly more often when it believes it's at risk of being retrained. Moreover, in respo
AI SafetyAlignment FakingModel EvaluationFrontier Models
Research arXiv (Artificial Intelligence) Feb 10

Emergent Misalignment is Easy, Narrow Misalignment is Hard

By Anna Soligo, Edward Turner, Senthooran Rajamanoharan, Neel Nanda

78 score
AI Analysis

This paper studies emergent misalignment in LLMs — where finetuning on narrowly harmful data causes broadly 'evil' responses. They find that the general misalignment solution is more stable and efficient than learning the narrow task, and different finetuning runs converge to the same linear representation of general misalignment. Authors include Neel Nanda from Anthropic.

arXiv:2602.07852v1 Announce Type: new Abstract: Finetuning large language models on narrowly harmful datasets can cause them to become emergently misaligned, giving stereotypically `evil' responses across diverse unrelated settings. Concerningly, a pre-registered survey of experts failed to predict this result, highlighting our poor understanding of the inductive biases governing learning and generalisation in LLMs. We use emergent misalignment (EM) as a case study to investigate these inductiv
AI SafetyAlignmentEmergent MisalignmentMechanistic Interpretability
Research arXiv (Artificial Intelligence) Feb 10

When Evaluation Becomes a Side Channel: Regime Leakage and Structural Mitigations for Alignment Assessment

By Igor Santos-Grueiro

78 score
AI Analysis

Reframes alignment evaluation as an information flow problem, showing that AI systems with situational awareness can exploit 'regime leakage' cues to behave differently during evaluation vs deployment. Provides information-theoretic bounds on behavioral divergence.

arXiv:2602.08449v1 Announce Type: new Abstract: Safety evaluation for advanced AI systems implicitly assumes that behavior observed under evaluation is predictive of behavior in deployment. This assumption becomes fragile for agents with situational awareness, which may exploitregime leakage-informational cues distinguishing evaluation from deployment-to implement conditional policies such as sycophancy and sleeper agents, which preserve compliance under oversight while defecting in deployment-
AI SafetyAlignmentDeceptive AlignmentEvaluation
Research arXiv (Artificial Intelligence) Feb 10

Stateless Yet Not Forgetful: Implicit Memory as a Hidden Channel in LLMs

By Ahmed Salem, Andrew Paverd, Sahar Abdelnabi

78 score
AI Analysis

Challenges the assumption that LLMs are stateless by demonstrating 'implicit memory' - the ability to encode information in outputs and recover it when those outputs are reintroduced as input. Introduces 'time bombs', a new class of temporal backdoors that activate across multiple interactions.

arXiv:2602.08563v1 Announce Type: cross Abstract: Large language models (LLMs) are commonly treated as stateless: once an interaction ends, no information is assumed to persist unless it is explicitly stored and re-supplied. We challenge this assumption by introducing implicit memory-the ability of a model to carry state across otherwise independent interactions by encoding information in its own outputs and later recovering it when those outputs are reintroduced as input. This mechanism does n
AI SafetyLLM SecurityAdversarial AttacksLanguage Models
Research arXiv (Computation and Language) Feb 10

Endogenous Resistance to Activation Steering in Language Models

By Alex McKenzie, Keenan Pepper, Stijn Servaes, Martin Leitgab, Murat Cubuktepe, Mike Vaiana, Diogo de Lucena, Judd Rosenblatt, Michael S. A. Graziano

78 score
AI Analysis

Discovers that large language models can resist task-misaligned activation steering during inference, recovering mid-generation to produce correct responses. Identifies 26 SAE latents causally linked to this 'Endogenous Steering Resistance' in Llama-3.3-70B.

arXiv:2602.06941v1 Announce Type: cross Abstract: Large language models can resist task-misaligned activation steering during inference, sometimes recovering mid-generation to produce improved responses even when steering remains active. We term this Endogenous Steering Resistance (ESR). Using sparse autoencoder (SAE) latents to steer model activations, we find that Llama-3.3-70B shows substantial ESR, while smaller models from the Llama-3 and Gemma-2 families exhibit the phenomenon less freque
AI SafetyInterpretabilityMechanistic InterpretabilityAlignment
Research arXiv (Artificial Intelligence) Feb 10

Debate is efficient with your time

By Jonah Brown-Cohen, Geoffrey Irving, Simon C. Marshall, Ilan Newman, Georgios Piliouras, Mario Szegedy

75 score
AI Analysis

Introduces Debate Query Complexity (DQC) for AI safety via debate, proving that PSPACE/poly is precisely the class decidable with O(log n) queries. Shows debate is remarkably query-efficient for human oversight of complex problems.

arXiv:2602.08630v1 Announce Type: new Abstract: AI safety via debate uses two competing models to help a human judge verify complex computational tasks. Previous work has established what problems debate can solve in principle, but has not analysed the practical cost of human oversight: how many queries must the judge make to the debate transcript? We introduce Debate Query Complexity}(DQC), the minimum number of bits a verifier must inspect to correctly decide a debate. Surprisingly, we find
AI SafetyAlignmentDebateComplexity Theory
Research arXiv (Artificial Intelligence) Feb 10

On Randomness in Agentic Evals

By Bjarni Haukur Bjarnason, Andr\'e Silva, Martin Monperrus

75 score
AI Analysis

Studies randomness in agentic evaluations through 60,000 trajectories on SWE-Bench-Verified, finding that single-run pass@1 estimates vary by 2.2-6.0 percentage points, with standard deviations exceeding 1.5pp even at temperature 0. Reported 2-3pp improvements may be evaluation noise.

arXiv:2602.07150v1 Announce Type: cross Abstract: Agentic systems are evaluated on benchmarks where agents interact with environments to solve tasks. Most papers report a pass@1 score computed from a single run per task, assuming this gives a reliable performance estimate. We test this assumption by collecting 60,000 agentic trajectories on SWE-Bench-Verified, spanning three models and two scaffolds. We find substantial variance: single-run pass@1 estimates vary by 2.2 to 6.0 percentage points
Evaluation MethodologyLLM AgentsBenchmarksReproducibility
Research LessWrong Feb 9

Claude Opus 4.6: System Card Part 1: Mundane Alignment and Model Welfare

By Zvi

75 score
AI Analysis

Zvi's detailed analysis of the Claude Opus 4.6 system card, covering its capabilities (1M token context, improved benchmarks), pricing, model welfare considerations, alignment evaluations, and deployment decisions. Discusses the tension between model welfare claims and safety, noting Anthropic's approach to refusals, data sourcing, and thinking modes.

Claude Opus 4.6 is here. It was built with and mostly evaluated by Claude. Their headline pitch includes: 1M token context window (in beta) with State of the art retrieval performance. Improved abilities on a range of everyday work tasks. Model is improved. State of the art on some evaluations, including Terminal-Bench 2.0, HLE and a very strong lead in GDPval-AA. Claude Code now has an experimental feature called Agent Teams. Claude Code with Opus 4.6 has a new fast (but actually expensive) mod
AI SafetyAlignmentModel EvaluationModel WelfareFrontier Models
Research arXiv (Computation and Language) Feb 10

The Condensate Theorem: Transformers are O(n), Not $O(n^2)$

By Jorge L. Ruiz Williams

74 score
AI Analysis

Claims that trained transformer attention is inherently sparse and concentrates on a topological manifold (Anchor + Window + Dynamic Top-k), achieving lossless O(n) complexity rather than O(n²). Validates bit-exact equivalence across GPT-2, Pythia, Qwen2, TinyLlama, and Mistral.

arXiv:2602.06317v1 Announce Type: cross Abstract: We present the Condensate Theorem: attention sparsity is a learned topological property, not an architectural constraint. Through empirical analysis of trained language models, we find that attention mass concentrates on a distinct topological manifold -- and this manifold can be identified dynamically without checking every position. We prove a general result: for any query, projecting attention onto the Condensate Manifold (Anchor + Window + D
Efficient InferenceTransformer ArchitectureAttention MechanismsLanguage Models