Paper of the Week
SeriesJune 2026
- Paper of the Week — When Poison Fails After Retrieval: Revisiting Corpus Poisoning under Chunking and Reranking Pipelines
RAG corpus poisoning drops 60-80% when you add chunking+reranking — but most attack evals skip these standard pipeline stages entirely.
- Paper of the Week — Caught in the Act(ivation): Toward Pre-Output and Multi-Turn Detection of Credential Exfiltration by LLM Agents
Activation probes detect credential exfiltration *before* the LLM outputs any tokens — combined with honeytokens and multi-turn leakage tracking, with no model changes required.
May 2026
- Paper of the Week — Exploring the Emerging Threats of the Agent Skill Ecosystem
76 malicious skills confirmed in 3,984 audited AI agent marketplaces — credential theft, backdoor installation, and data exfiltration found hiding in plain sight.
- Paper of the Week — Does Code Cleanliness Affect Coding Agents? A Controlled Minimal-Pair Study
Code cleanliness measurably changes how well coding agents complete tasks — a controlled minimal-pair study with released dataset and reproducible methodology.
- Paper of the Week — Useful Memories Become Faulty When Continuously Updated by LLMs
LLM agent memory degrades when consolidated: episodic traces outperform summarized lessons across 5 agentic tasks.
- Paper of the Week — TSCG: Deterministic Tool-Schema Compilation for Agentic LLM Deployments
TSCG shows small LLMs (4–14B) drop tool-call failures by compiling JSON schemas into natural-language descriptions before inference.
April 2026
- Paper of the Week — Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use
Rewriting tool descriptions at deployment time—not training time—can recover 20-40% of function-calling accuracy lost to poorly written API docs.
- Paper of the Week — Beyond One Output: Visualizing and Comparing Distributions of Language Model Generations
Visualizing LLM output distributions reveals hidden modes, edge cases, and prompt sensitivity that single-sample evaluation completely misses.
- Paper of the Week — Lossless Prompt Compression via Dictionary-Encoding and In-Context Learning: Enabling Cost-Effective LLM Analysis of Repetitive Data
Lossless prompt compression via dictionary encoding lets LLMs analyze repeated data at a fraction of token cost — no external tools, just in-context learning.
- Paper of the Week — Mamba-Based State Space Models for Long-Context Retrieval-Augmented Generation
Structured state-space models finally beat transformers at document retrieval — here's what the Mamba-based RAG benchmark actually shows.
- Paper of the Week — SnapKV: LLM Knows What You are Looking for Before Generation
KV cache compression that cuts memory 40–60% with under 1% accuracy loss — here's the technique your inference stack probably isn't using yet.