Using a bug-fix coding task, Graham Neubig takes Lecture 9's policy gradient into practice: a critic, GAE, or a PRM to credit individual turns; importance ratios and clipping to handle stale data in async RL; and PPO, GRPO, CISPO, GSPO, and DAPO side by side in one table. The largest share goes to the reward itself — verifier errors, reward hacking, and exploration collapse — before closing with on-policy distillation.
learn-inference.com is an unofficial interactive companion to Philip Kiely's Inference Engineering (256 pages, Baseten Books, free PDF). It follows the book's 8 chapters and 42 sections with rewritten explanations, turns intuition-heavy ideas like TTFT, P99, speculative decoding, and prefix-cache routing into slider-driven simulators, and ships a keyless JSON API and MCP server.
Obscura is a headless browser engine written from scratch in Rust. It embeds V8 and speaks the Chrome DevTools Protocol, so Puppeteer and Playwright scripts connect by changing one endpoint, and it ships an MCP server with 37 tools. The headline numbers (about 30 MB per process, about 85 ms per page load) come from the project's own benchmarks; it passes 83.3% of its core WPT subtests, and long-tail CSS and some Web APIs still differ from Chromium.
Traditional chat only needs text bubbles, but AI Agent conversations must surface thinking, tool calls, citations, and progress — we solved this with 12 Vue components, three DisplayModes, and a unified chatBlocks rendering pipeline.
In H1 2026, OpenAI, Anthropic, Letta, and LangChain independently chose Markdown files + indexes over vector databases; write permissions shifted back to humans; forgetting mechanisms appeared but nobody implemented Ebbinghaus; Penfield Labs caught 6.4% wrong answers in LoCoMo; three vendors simultaneously adopted 'Dreaming' for offline memory consolidation. Five trends, one conclusion: memory is not a feature — it is an architecture decision.
Memory turns prompt injection from a one-shot nuisance into a persistent backdoor: MINJA shows conversation-only injection succeeds >95% of the time, and SpAIware demonstrated continuous data exfiltration via planted memories. The industry's two defensive lines — citation-based verification (Copilot) and human approval inboxes (Gemini CLI / Devin) — each have blind spots.
Agent memory is not one feature — it is at least four distinct engineering problems: working, episodic, semantic, and procedural. This ten-part series walks through the full design space, from taxonomy to coding agent implementations, platform APIs, open-source frameworks, security attack surfaces, and 2026 trend analysis.
CoALA splits agent memory into working, episodic, semantic, and procedural — but the four-cell taxonomy alone doesn't explain why Claude Code uses Markdown files while Mem0 uses vectors. This post adds six independent design axes (read mode, write timing, fidelity, write authority, forgetting, scope) and a file-to-graph spectrum to map the full design space of agent memory systems in 2026.
The previous articles covered evaluation. This one covers another dimension: how to dynamically allocate compute during reasoning. BrowseConf's core insight is that an agent's self-declared 'confidence' can predict answer accuracy. High confidence uses fewer resources; low confidence searches more rounds.
All five major cloud platforms shipped agent memory APIs in 2025–2026, but their design philosophies diverge sharply: OpenAI writes memory as files, Anthropic mounts memory as a directory, Google uses vectors with topic classification, AWS combines events with pluggable strategy pipelines, and Microsoft abstracts memory behind context providers. Pricing ranges from free to $0.75/1K records/month; tenant isolation spans from 'your app handles it' to IAM as a first-class citizen.
Nine coding agents have taken at least four different paths for long-term memory: Claude Code and Codex use Markdown files (agent-written, human-readable), Antigravity CLI inherits Gemini CLI's approval inbox (agent proposes, human decides), and Copilot uses citations with JIT verification (auto-deleted after 28 days unverified). Cursor removed its Memories feature and fell back to human-written Rules. Hermes Agent, OpenClaw, and Letta Code treat memory as a core harness component, not a plugin. Their choices on write timing, forgetting, and cross-team sharing are completely different — and none has published a controlled experiment on whether their memory system actually helps.
By 2026, the deep research commercial market has differentiated: OpenAI is comprehensive, Perplexity is fast, Gemini integrates ecosystems, Claude reasons deeply, Grok is real-time. This article compares each product's differences—not who is best, but who fits your scenario.
The entire Deep Research field has 80+ implementations, but the core structure is just three-stage roadmap × four components × three optimization methods. This article maps the full landscape: from Agentic Search to Full-stack AI Scientist, from query planning to answer generation, from workflow prompting to end-to-end RL.
DeepResearch Bench II uses 9,430 expert rubrics covering 132 tasks, and finds that even the strongest agents satisfy less than 50% of criteria. This article breaks down the benchmark architecture, scoring methodology, leaders, and the overall evaluation landscape.
10+ community deep-research skills represent 10+ philosophies of 'how to do research.' From hyperresearch's persistent vault to jamoeight v2's Co-Scientist 6-agent, from adversarial verification to benchmark alignment. This article puts them all on one table.
A deep research agent produces a report—maybe thousands of words with dozens of citations. How do you score it? Using LLMs as judges is biased, asking humans is too expensive, and benchmarks can't keep up. STC and other recent approaches try to solve this from the 'confidence' angle—but there's no perfect answer yet.
Deep research has already evolved from 'help you search' to 'help you research.' But the next step is bigger: self-evolving agents, swarm collaboration, scientific automation. This article covers three directions and an uncomfortable reality: Gartner predicts 40% of agent projects will be cancelled by 2027.
When a research agent runs 25, 100, or 2000 turns, what happens? Context suffocation: information piles up, noise increases, attention gets diluted. IterResearch solves this with Markovian state reconstruction; AREX achieves recursive self-improvement with an inner/outer loop. Both answer: how does an agent stay coherent across hundreds of search rounds?
Seven open-source memory frameworks span the spectrum from auto-extracted vectors to human-readable files: Mem0's one-line add(), Graphiti's bi-temporal knowledge graph, Letta's agent-edited system-prompt blocks, LangGraph's namespaced Store, LlamaIndex's priority-based block truncation, Cognee's triple-store pipeline, and Supermemory's temporal vector-graph engine. This post compares their storage, write/forget mechanics, tenant isolation, and benchmark numbers, then offers selection guidance for four common scenarios.
The deep research open-source ecosystem has evolved from 'single frameworks' to 'tool clusters.' This article compares 12+ projects: GPT-Researcher emphasizes multi-agent collaboration, STORM simulates expert conversations, smolagents focuses on state management. Each tool solves different problems.