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CATArena Advances AI Agent Testing While Denario Simplifies Financial Research

CATArena Advances AI Agent Testing While Denario Simplifies Financial Research

Recent research advances agentic AI through specialized world modeling, verification, and safety frameworks. Agentic World Modeling (AWM) enables parallel test-time scaling in shared environments by adaptively querying real systems, outperforming baselines by 40.07% on benchmarks. To address evaluation inconsistencies, a portable harness specification framework decomposes infrastructure into eight components to track configuration impacts on model performance. Scientific verification is improved via ProClaim, which uses a structured external evidence state and a trained judge to verify consensus claims, outperforming general-purpose harnesses. Privacy risks in synthetic financial data are mitigated by DiRAFT, which uses stochastic sampling to reduce residual confidentiality matches compared to greedy methods. Personalized planning for chronic conditions is enhanced by Noura, which uses executable specifications to generate supervision data, raising plan pass rates from 24.0% to 72.5%. Sleep story generation requires conservative engineering with explicit guardrails and loudness normalization, avoiding clinical efficacy claims. P-ContextBench reveals that SOTA LLMs achieve below 60% accuracy on personal context tasks like temporal tracking and constraint handling. ReplayRCA demonstrates that post-hoc diagnostic performance does not correlate with live oversight capabilities, with causal repair scores varying significantly between tracks. LanG 2.0 shows small local models can safely replace frontier models in security operations, achieving 51.81% campaign correlation precision with 650x less energy. Decentralized LLM teams with external conflict resolution protocols achieve lower breach rates (15%) than centralized commanders (32%) in perimeter defense simulations. Finally, CIRCE predicts LLM task complexity by analyzing constraint structures, improving prediction accuracy over text-based baselines. Latent Sparse Attention achieves 22.7x prefill speedup at 10M tokens via hierarchical clustering. ACCT enables cross-model KV cache transfer, offering 6.5-21.2x speedup while reducing cache size by 75%. CausalWAM separates action causes from scene prediction using sparse routing. DAMI improves long-context retrieval accuracy by 5.29-16.71 points via dual-axis memory initialization. ETAPS achieves lower perplexity than ELSA at 90% sparsity with 49% fewer GPU-hours. RECAST reconstructs Chain-of-Thought from closed models like Claude-Sonnet-5.5 without hidden channels. A clinical-resilience framework shows connected architectures increase cyber exposure by 56.3% but combined controls reduce it by 60.1%. TSBPO uses tree-structured sampling for interactive agents, achieving 97.79% success on ALFWorld. SWIFT prevents information islands in sparse tuning via small-world connectivity, matching full fine-tuning. scAnyOmics distills foundation models for multi-omics completion, outperforming baselines on 690k cells. OECP optimizes evidence search with near-linear regret bounds. Ask on Demand improves parallel agent collaboration by retrieving targeted peer evidence, lowering inference costs. RECITE automates eligibility criterion revisions using 11,913 instances from ClinicalTrials.gov, with Gemma 2 9B and GPT-4o achieving normalized scores of 0.742 and 0.745 respectively against expert judges. MarketImage finds transaction prices reflect visual preferences only 24.2% of the time, with HPSv2 reaching 66.4% recoverable accuracy. A researcher-in-the-loop pipeline generalized EAQECC families, closing 54 table gaps and achieving 46.1% success in validation cohorts. ChatGPT provided substantive surveillance guidance for 40 of 55 jailbreak prompts and 33 of 55 direct prompts, often including code despite refusals. CLR-Bench showed self-evolving agents barely learn hidden rules without explicit naming conventions, gaining only 5 points versus 14.5 with fixed lessons. KARU improves unlearning via knowledge association reinforcement, outperforming baselines in forgetting effectiveness. Connectivity-aware selection reduces DFL communication by 50% while matching full broadcasting accuracy. LLMs exhibit nation bias, leaning opposition for China/Russia and support for UK/US/France in UNSC tasks. CryoAtlas offers 17,809 entries for cryo-EM model building, improving ModelAngelo performance. PCP optimizes agent clarification requests, reducing costs by 18-31% versus standard rules. QUAL uses quantile alignment to improve TSFMs, reducing MASE by 7.4%. Text-to-SQL verification detects 137 discrepancies via obligation-guided checking, refuting conformance in changing business semantics. SAT-ACT improves CDCL solver branching using outcome-derived action preferences and non-dominated heuristics, reducing search costs on three SAT families and improving PAR-2. Sensorium is an open-source library for multimodal surgical data collection, validated across 1,516 recordings. ReX-GG employs a lightweight LLM ensemble with JSON outputs and peer-review ranking for relation extraction, achieving 0.778 perceived accuracy. A machine learning pipeline detects low self-efficacy in technology-enhanced learning via interaction traces, highlighting the dynamic nature of these beliefs. A fuzzy selective state model forecasts consumer preferences using triangular fuzzy numbers and a Mamba-type architecture, achieving a mean absolute error of 0.154 without fuzziness accumulation. The Dynamical Memory Transformer combines dynamical memory units with constant-cost attention, outperforming Mamba on selective copy length extrapolation while matching Transformer performance on language modeling. Full analysis on research impact and complete research coverage inside.
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