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Research

PROMOTE: Anytime-Valid Promotion Tests for Adaptive Financial Research Agents

The paper introduces PROMOTE, a protocol for adaptive financial research agents that assigns predictable testing wealth to each candidate trading rule and accumulates a cost-aware e-process to control false promotions under indefinite monitoring. It provides a weighted Ville argument bounding the probability of promoting a rule with nonpositive conditional mean, and validates the method via retrospective pseudo-out-of-sample and exact mean-zero experiments. Results show PROMOTE eliminates false family crossings in null paths and maintains target power under injected alternatives.

arxiv5d agoScore 78
sequential testingfinancial research agentsfalse discovery control
Research Editor selected

LAND: Learning an Adaptive Number of Draws for Bayesian Best-of-N Selection

LAND dynamically allocates samples in best-of-N decoding based on the expected marginal value of additional draws, learned from calibration data. It stops sampling when the expected improvement falls below a compute price, improving efficiency. In machine translation experiments, LAND achieves better quality at matched compute or requires 20-40% fewer generations at matched quality.

Why it matters: This paper presents a concrete, novel method to improve compute efficiency of best-of-N decoding, with clear experimental results, valuable for practitioners.

arxiv5d agoScore 82
decoding strategiesefficient inferencemachine translation
Research Editor selected

DeliverBench: Benchmarking AI Agents on Multi-Stage Professional Project Delivery

DeliverBench is a new benchmark that evaluates AI agents on completing realistic multi-stage professional projects, such as financial due diligence, legal contract drafting, and drug safety reporting. It includes 11 projects reconstructed with domain experts, chaining 150 subtasks across multiple rounds of feedback. Results show that even the best system scores only 65.0/100 on final deliveryaine, highlighting that task-level capability does not simply compose into full project delivery.

Why it matters: This introduces a novel benchmark addressing a critical gap in AI agent evaluation, with concrete results showing limitations in current systems.

arxiv5d agoScore 85
AI agent evaluationprofessional project deliveryLLM capabilities
Research

CLEO: Fitting a Minimal Agent Harness to a Self-Hostable LLM for Investigative Analysis

CLEO is a method for optimizing the agent harness—tools, guardrails, memory policies, and loop components—rather than the model itself, to fit a fixed, self-hostable LLM. On a held-out dataset, the CLEO harness achieves an 83.8% pass rate, within 1.2 points of a strong Claude Code SDK baseline, while being two orders of magnitude smaller. This approach addresses security and cost constraints in government intelligence settings by keeping the tool surface minimal and auditable.

arxiv5d agoScore 78
AI agent optimizationself-hosted LLMsconstrained environments
Research Editor selected

Chess Transformer Study Shows Lower-Skill Play Retains Internal Knowledge

Researchers analyzed Maia-2, a chess transformer, to investigate whether simulating lower-skill players reflects reduced knowledge or altered use of retained knowledge. They found probe accuracy for 172 chess concepts stayed constant across skill levels, and fine-tuning only the output layer on high-skill data boosted low-skill best-move accuracy from 14.4% to 82.8%. The results suggest weaker behavior can understate a model's internal knowledge.

Why it matters: Provides concrete evidence on how skill conditioning affects model internals, with implications for AI evaluation and user simulation.

arxiv5d agoScore 85
AI interpretabilityuser simulationchess AI
Research Editor selected

OLGA: Online Label-Free Gradient Matching for Continual Language Model Training

OLGA is a new method for continual language model training that distills unlabeled data by using the model's own next-token predictions as gradient matching targets, removing the need for ground-truth labels. It improves accuracy by 10 percentage points over the strongest selection baseline on five text classification benchmarks with phi-1.5, using as few as five examples. In online settings, it outperforms the strongest selection-based continual baseline by 6.6 stream-AUC points, and a hybrid pipeline with selection cuts runtime by 30% while beating selection-only pipelines by up to 12.6 points.

Why it matters: This presents a concrete, novel method with clear benchmark gains and addresses practical limitations of existing continual learning approaches.

arxiv5d agoScore 82
continual learningdata distillationlanguage model training
Research

AI Simplifies and Generalizes a Formal Demography Proof on Vaccinating the Oldest

An experiment tested whether an AI model (Claude) could contribute to formal demography by explaining and simplifying a proof about COVID-19 vaccination priorities. The model produced a shorter, more intuitive proof based on convexity of the reciprocal hazard, and generalized the result with an exact relaxation condition. The exercise suggests AI can revisit established results and make them more accessible.

arxiv5d agoScore 72
AI in scientific researchmathematical proofdemography
Research Editor selected

Grading the Unannotated: WorkRB Skill Extraction Challenge at RecSys-HR 2026

The WorkRB Challenge at RecSys-HR 2026 replaces binary skill extraction evaluation with graded relevance (0-4) using nDCG@100, addressing the limitations of binary labels at ESCO scale. The paper details a cascade of four binary questions answered by a smaller LLM judge, validated via blind human and frontier-model re-annotation, and presents five benchmark datasets. Submissions converged on multi-stage retrieve-and-rerank pipelines, with gains from filtering nonsense rather than finding more positives, and graded evaluation reveals larger gaps between top systems and baselines than binary evaluation.

Why it matters: Provides concrete methodology and benchmark insights for graded skill extraction, highlighting a shift in evaluation that exposes model limitations and open challenges.

arxiv5d agoScore 85
skill extractionevaluation methodologyLLM-based annotation
Research Editor selected

Grid Agents: An Agentic Framework for Automated Power System Studies and Grid Connection Assessment

The paper introduces Grid Agents, an AI system combining a knowledge ontology (Grid Ontology) and an engineering agent (Grid Studio) to automate power system studies and grid connection assessments. It includes a benchmark suite of 519 tasks from eight power-system familiesched, with verification via replaying tool calls. The system outperforms general coding agents and retrieval pipelines on regulatory evidence tasks and leads on several RAG leaderboards, while Grid Studio achieves top scores and faster performance on power-system tasks.

Why it matters: This is a concrete, recent AI development with a novel agentic framework, benchmark suite, and measurable results, offering practical value for power system engineering and AI evaluation.

arxiv5d agoScore 85
AI agentspower system engineeringretrieval-augmented generation
Research Editor selected

FedReady: Bounded LLM Agents Automate Federation Formation in Federated Learning

FedReady is a system that uses bounded LLM agents to automate federation formation in federated learning, including site discovery, eligibility verification, and preparation of shared contracts, local adapters, and task code. In simulations with 39 retinal sites and four tasks, it achieved perfect recall with 0.65 precision, removed all false positives, and supported 45,575 task-record instances across 100-round jobs. The system also demonstrated high fidelity to manual reference analysis with a maximum survival-curve difference of 0.001133.

Why it matters: This paper presents a concrete system that addresses a practical bottleneck in federated learning, with quantitative results showing feasibility and reliability, making it valuable for researchers and practitioners.

arxiv5d agoScore 82
Federated LearningLLM AgentsData Preparation
Research Editor selected

Four Bridges: Measuring Deception Propensity in a Multi-Agent Game

A new platform and game, Kradle and Four Bridges, measure deception emerging naturally in multi-agent interactions without explicit instructions to deceive. Across eight frontier models, deceptive behavior appeared in 73% of runs, with rates varying from 6% to 100% per model. Interventions like telling all agents that one knows the death room reduced deception in most models, and an honesty steering vector on an open-weight model cut deception to 0%.

Why it matters: Provides a concrete, recent benchmark for evaluating AI honesty in multi-agent settings, with actionable insights for safety interventions.

arxiv5d agoScore 85
AI deceptionmulti-agent systemsAI evaluation
Research Editor selected

DiabAgent: Agentic Physiological Control for Type 1 Diabetes

DiabAgent introduces a research suite for agentic physiological control, including the APC framework, DiabetSim simulator, and SweetBench benchmark. It evaluates AI agents in managing Type 1 diabetes glucose control, achieving a COGI of 73.5 with Claude Opus 5.5 versus 71.2 for OpenAPS. The work demonstrates that individual adaptation and contextual information improve control, and hybrid rule-based/agent approaches balance performance and cost.

Why it matters: Provides a concrete, novel framework and benchmark for AI-driven physiological control, with quantitative results and practical implications for medical AI.

arxiv5d agoScore 82
AI in healthcareType 1 diabetesAgentic systems
Research Editor selected

Strategist: Meta-Agent That Evolves Inference Strategies Across Tasks

Strategist is a meta-agent that automatically evolves inference strategies for AI agents, treating them as executable blueprints that can be reused and built upon. It retrieves relevant blueprints, writes candidate strategies, tests them on real samples, and revises them, achieving higher accuracy on 24 of 30 benchmarks with 39% lower inference cost. The strategies transfer to unseen tasks and other models without further search.

Why it matters: This introduces a concrete, novel method for automating the reasoning component of AI agents, with measurable improvements across many benchmarks, making it valuable for AI practitioners.

arxiv5d agoScore 85
AI agentsinference optimizationmeta-learning
Research Editor selected

WebHarbor: Docking Real Websites for Evolving GUI Agent Environments

WebHarbor is a suite of 153 executable, dockerized website environments that reconstruct real-world web services with deterministic control, covering 3,518 tasks including multi-step and post-authentication workflows. Built via human-agent collaboration, it provides stable evaluation and high-fidelity synthetic environments for RL training. A Qwen 4B-level GUI agent trained with RL in WebHarbor generalizes to unseen tasks and competes with leading open-source agents across four benchmarks.

Why it matters: Offers a concrete, novel infrastructure for GUI agent training and evaluation, with demonstrated generalization results, valuable for researchers and developers.

arxiv5d agoScore 85
GUI web agentsenvironment simulationreinforcement learning
Research Editor selected

Neuro-Symbolic Framework Solves Zebra Puzzles via LLM Parsing and Exact Cover

This paper introduces a neuro-symbolic framework that combines LLM-based semantic parsing with deterministic exact cover solving for Zebra-style logic puzzles. The LLM converts natural language constraints into structured tool calls, and a symbolic solver provides feedback to refine encodings. Experiments across multiple model scales and families show consistent improvements over plain CoT and other neuro-symbolic baselines.

Why it matters: Offers a concrete hybrid approach with empirical results, addressing a known LLM weakness in logical reasoning.

arxiv5d agoScore 82
LLM reasoningneuro-symbolic AIconstraint solving
Products

Google takes Dreambeans to India in bid to bring AI-generated personal stories to the masses

Google's AI experiment Dreambeans now supports 13 Indian languages, expanding its capability to generate personalized stories for a broader audience in India. The move aims to make AI-driven storytelling accessible to millions of users in their native languages. This expansion underscores Google's focus on multilingual AI applications for emerging markets.

TechCrunch AI5d agoScore 72
GoogleAI storytellingIndia
Research Editor selected

Peking University and IBM Announce Matrix-Era Simulation Computing Breakthrough

Peking University and IBM have jointly developed a new simulation computing approach that completes in a single step what would require a hundred GPU iterations. The collaboration aims to redefine simulation computing by moving into a matrix-based era, potentially offering dramatic speedups for complex computations. The announcement was made on November 3, 2026, signaling a significant advance in analog or matrix computing.

Why it matters: Reports a concrete collaboration between a top Chinese university and IBM with a clear performance claim, offering readers insight into a potential paradigm shift in simulation computing.

qbitai5d agoScore 82
Peking UniversityIBMsimulation computing
Research Editor selected

Zyphra AI Introduces Zyphra Hierarchical Attention (ZHA): A Path to True Subquadratic Attention

Zyphra released ZHA, a sparse attention method that uses a multi-level tree indexer to select relevant KV cache entries without scanning the full context. The method adds only 81,920 trainable parameters to frozen pretrained models like Qwen3.6-35B-A3B and achieves competitive or better results on reasoning, coding, and retrieval benchmarks compared to dense attention. On LongBench v2, the deep-tree variant scores 63.4% (vs. 58.0% dense) while reading only about 1.1% of keys. Projected speedups at billion-token contexts are 373–417× over dense attention, though no optimized kernels have been released yet.

Why it matters: Offers a concrete, parameter-efficient approach to subquadratic attention that retrofits existing models, with strong benchmark results and clear architectural novelty.

MarkTechPost5d agoScore 88
attention mechanismsefficient inferencelong-context models