Research and tooling are concentrating on how LLM-based agents coordinate, share context, plan and verify outputs. Multi-agent single-model frameworks, shared KV caches, skill libraries, fact-checking pipelines and controlled prompt evolution point toward more reliable autonomous workflows.
FinEvolveKG separates general rules from persona policies and confines the reflective prompt optimizer GEPA to designated blocks, keeping the approved block byte-stable. A dual objective balances generic temporal quality with persona-relevant facts. ...
A user asks whether anyone uses the 'none' reasoning level in agentic tasks. The temptation is to save token generation time, but the fear is losing planning. For self-hosted LLM operators, this parameter touches VRAM, latency, and cost per successfu...
A single-model multi-agent framework shares one backbone and coordinates GPU-resident KV states through a directed acyclic graph. Zero-copy radix forking, attention-only KV mounting, and packed DAG-GRPO training cut peak KV memory by 2.8x and raise t...
A knowledge-data dual-driven approach updates LLM skill libraries for liability adjudication tasks. KDASO addresses stale skills, semantic execution gaps and context overflow; tests show gains and transfer to similar scenarios.
A team presented a four-stage pipeline that generates fact-checking articles from a claim, the original verdict and web-scraped sources. Llama-3.1-8B-Instruct produces the text, while deterministic post-processing recovers citations. Mean score 0.428...
