AWS announced HippoRAG, a neurobiologically inspired retrieval augmented generation framework using Amazon Bedrock, Amazon Neptune, and Personalized PageRank to improve multi-hop reasoning in large la…
Researchers introduced Contrastive Reflection, an iterative prompt-optimization framework for agentic information retrieval workflows, which uses error-anchored behavioral slices and contrastive examp…
Maneshwar, developer of git-lrc, explains the ReAct (Reasoning + Acting) prompting pattern from Yao et al., 2022. ReAct interleaves reasoning traces with actions like search or lookup, allowing langua…
Researchers introduced Decoupled Search Grounding (DSG), a vendor-agnostic architecture that separates search from reasoning in LLM agents, enabling independent control over retrieval policy, provider…
Researchers at arXiv propose Telegraph English, a readable symbolic format for context compression that rewrites retrieved passages into structured entity-relation statements. In experiments on multi-…
Researchers have identified a new failure mode in multi-step agentic retrieval-augmented generation (RAG) systems called cascading hallucination, where errors from early pipeline stages propagate and …
Headroom, a new macOS menu bar app, reduces Claude Code token usage by approximately 50% by locally trimming prompt bloat and boilerplate before inputs reach the model. The tool processes all data on …
Researchers have demonstrated that compressing tool schemas by 44-50% enables agentic retrieval-augmented generation (RAG) systems to function under tight context budgets, where uncompressed definitio…
Researchers introduced parallel context compaction for long-horizon LLM agent serving, addressing the problem of growing conversation histories exceeding context windows. The method provides fine-grai…