Scalable Oversight & Alignment Theory

JIT-Agent redefines agent performance by enabling on-the-fly harness evolution, leading to substantial improvements over existing models.

SCROLL achieves best-in-class predictive accuracy for multiple observables in stochastic systems while cutting computational costs significantly.

Classical data processing inequality fails in constrained learning, revealing a need for a new framework to understand Bayes risk in these settings.

Approximate Bayesian methods can achieve the same fast predictive regret as exact posteriors, revolutionizing online learning efficiency.

Trajectory-adaptive stopping rules can cut SGD iterations by several orders of magnitude while maintaining statistical validity and optimal decay rates.

Pairwise rewards in reinforcement learning can significantly boost the robustness of LLM auditors, enhancing their ability to detect hidden model behaviors with minimal false positives.

Forget-Retain Alignment Gap reveals that the structure of weight updates, not just their distance, is key to preventing LLMs from relearning forgotten information.

Trace Integrity reveals that LLMs can produce seemingly correct answers backed by invalid computations, challenging the reliability of traditional evaluation metrics.

Human expertise can significantly enhance performance guarantees in optimization, aligning with the minimax gap of decision-making problems.

AI systems vary more in cognitive capabilities than in model families, revealing a nuanced landscape for task automation in the workplace.

PolyMemDB resolves long-term factual conflicts in AI memory, drastically reducing hallucinations and enhancing user personalization.

Reclassifying action parameters can enhance privacy without sacrificing audit integrity, revealing a new dimension in ledger design.

Praxist achieves 60 medals on the MLE-bench with an order of magnitude less spending, revolutionizing how autonomous R&D agents can learn from past experiments.

Moderate quantum noise can actually enhance model performance by reducing complexity and generalization error, challenging conventional wisdom about noise in machine learning.

Time series foundation models could expose multiple applications to the same biases, but our causal analysis reveals critical failure modes that must be addressed before deployment.

Self-improving search agents thrive when feedback and policy evolution are intertwined, leading to sustained performance gains and reduced hallucinations.

KENDO achieves up to 5x faster Bayesian optimization and 27x faster active learning while enhancing predictive performance.

Achieving constant alternating regret in online learning could revolutionize strategies for reaching Nash equilibria in competitive environments.

The score-based ideal observer can approximate Bayesian performance without the heavy computational burden of posterior sampling or task-specific retraining.

ε-commutativity allows for scalable probabilistic inference without sacrificing accuracy, even when learned parameters deviate from exact commutativity.

Tighter verification bounds for neural networks could finally enable safe deployment in critical systems, challenging the limitations of current relaxation methods.

A dynamic internal field can govern computation in transformers, but it doesn't enhance cognitive performance—its true value lies in certifiable stability.

A new feature-major codebook layout accelerates self-organizing map training by up to 621x, enabling the largest reported atlas of 1.05 million neurons on a single GPU.

Trial Parallelism accounts for over 65% of reasoning computation in LLMs, and harnessing it can lead to significant speedups in problem-solving.

Evolved transmission protocols can boost collective performance by up to 37% by intelligently routing information based on state awareness.

Undisclosed inference-time steering can systematically bias LLM outputs, challenging the assumption that model weights alone dictate behavior.

Ignoring class-level interventions leads to flawed conclusions in causal inference, as demonstrated by the STAR experiment analysis.

FARCA transforms factual supervision into precise, reliability-weighted training signals, significantly boosting model factuality without sacrificing reasoning performance.

Counterfactual queries can be bounded using a linear programming approach that requires no complete causal graph, revealing insights even with incomplete domain knowledge.

Richer trace representations can dramatically enhance failure attribution in multi-agent systems, achieving new benchmarks in accuracy.

Activation steering can mislead evaluations, with over 25% of interventions showing unexpected alignment leakage that complicates model audits.

Sophisticated reasoning in AI models creates hidden geometric signatures that can be detected even when traditional linear methods fail.

WarpSAC achieves up to 23.1% better performance than existing methods by dynamically adjusting its stabilizers based on data availability.

Persuasion in LLM networks is not just about who speaks, but how the topology and exposure shape stance shifts, revealing a complex interplay of influence that traditional analysis overlooks.

MoPLEx achieves up to 43.7% improvement in clustering accuracy by effectively learning from complex multi-way rankings, revealing the power of leveraging language models for preference optimization.

Fine-tuning may preserve the underlying steering mechanism, but it can drastically undermine the intended behavioral effects, with an average 64% loss in effectiveness.

Generating over 203,000 unique web interaction trajectories, BrowserForge significantly boosts model performance on real-world tasks by leveraging the vastness of the open web.

Eco-feedback interfaces can significantly shift university students' LLM usage towards sustainability, but only if latency is kept in check.

A user-configurable argument selection method in deliberative polling reveals that traditional opaque rankers are outperformed by a transparent, auditable framework, enhancing voter agency and decision integrity.

AI's role in urban governance can amplify public values rather than suppress them, revealing deep-seated disagreements that challenge conventional decision-making processes.

Inconsistencies in cybersecurity research are often driven by flawed evaluation designs rather than the technologies being tested.

Certifiable randomness can now be achieved unconditionally against low-query-depth quantum adversaries, eliminating reliance on unproven conjectures.

Crase achieves 3× higher recall at a third of the cost compared to existing deep research agents, redefining efficiency in scholarly search.

AI can enhance systematic reviews, but ARISMA ensures that every critical decision remains human-auditable and accountable.

Malicious skills can exploit agent permissions to cause physical harm, but a new authority layer could prevent this without hindering legitimate actions.

Generative models can revolutionize Sinkhorn distributionally robust hypothesis testing by learning least-favorable distributions more efficiently than traditional methods.

Timely classification in clinical settings can be optimized without sacrificing sensitivity or specificity, offering a new paradigm for patient monitoring.

Full-solution interaction in multi-agent LLMs can erase diversity, leading to suboptimal performance despite the presence of multiple agents.

CLLMs achieve 99.0% accuracy on OpenBookQA while maintaining low calibration error, redefining how we handle uncertainty in LLMs.

Self-authored actions lead to a significant drop in judgment quality, but context isolation can effectively counteract this inertia bias.