WildToolBench: Why No LLM Exceeds 15% Session Accuracy in Real-World Tool Use
WildToolBench finds no LLM exceeds 15% session accuracy on 1,024 real-user tasks, with hidden intent and instruction transitions the sharpest failure modes.
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Технологии
Технологични изследвания и софтуерно инженерство, свързани с финансови ИИ системи
Lost in the Middle: Position Bias in LLMs and Its Impact on Finance AI
LLMs score up to 20 points worse when the answer sits mid-context, so finance RAG pipelines should place the best passages first or last.
OSWorld: Desktop AI Agents Succeed on 12% of Tasks Where Humans Succeed on 72%
OSWorld finds desktop AI agents succeed on 12.24% of real tasks versus 72.36% for humans, with 75% of failures from visuomotor grounding, not reasoning.
StructRAG (ICLR 2025): Picking the Right Document Structure Beats GraphRAG by 28 Points
StructRAG routes each query to a table, graph, catalogue, algorithm, or chunk structure, beating GraphRAG by 28 points and running 22× faster.
Single-Agent: equals MAS on multi-hop at equal tokens
Under equal thinking-token budgets, single-agent LLMs match or beat multi-agent systems on multi-hop reasoning—favor simpler finance agent designs.
Self-RAG: Adaptive Retrieval and Self-Critique for LLMs
Self-RAG trains an LLM to decide when to retrieve and self-grade results, hitting 55.8% on PopQA and 80.2 FactScore — beating ChatGPT on five benchmarks.
AgentBench: Оценка на LLM като агенти — Уроци за надеждността на финансовия ИИ
AgentBench даде на GPT-4 4.01 срещу 0.96 за най-добрия отворен LLM. Тези провали са точно това, което чупи агент за запис в Beancount върху активна книга.
MemGPT: 92.5% multi-session recall vs 32.1% baseline
MemGPT's OS-style memory tiers push GPT-4 multi-session chat accuracy to 92.5% versus 32.1% fixed-context—needed for multi-year ledger agents.