New Benchmark: The Struggle Bench
A new benchmark called "The Struggle Bench" tests AI models by placing them in a simulated apartment with a server,
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AI Agents Went From 13% to 90% in One Year. Every “Safe” Bug Class Now Has an Expiry Date.
In early 2025, the leading autonomous agent could exploit only 13% of critical web vulnerabilities on the CVE‑Bench benchmark. By mid‑2026, performance has surged to 90% exploitation, reflecting rapid advancement in AI‑d…
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Build an AI Agent with a Hippocampus: Implementing Sparse Distributed Memory in Python
To address the limitations of LLM context windows and the approximate nature of vector databases, this article details the implementation of Sparse Distributed Memory (SDM), based on Pentti Kanerva's 1988 model. Unlike s…
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Iris: How SFT-RL Climbing Trains Open-Weight Agents to Search the Web Like a Researcher
AllSpark Research has introduced Iris, a series of open-weight search agents comprising Iris-mini (35B total, 3B active) and Iris-pro (397B total, 17B active), both derived from the Qwen3.5/3.6 MoE series. To address the…
→ View original sourceTencent /WeKnora
Tencent has developed WeKnora, an open-source, LLM-powered framework designed for enterprise document understanding, semantic retrieval, and reasoning. The system integrates three core functional modes: RAG-based Q&A…
→ View original sourceThe-Swarm-Corporation /AutoHedge
AutoHedge, developed by The-Swarm-Corporation, is an enterprise-grade autonomous trading system designed to function as an AI-driven hedge fund. Utilizing a multi-agent swarm intelligence architecture, the platform autom…
→ View original sourcebrowser-use /browser-use
Browser-use is an open-source framework available in Python and TypeScript designed to enable AI agents to autonomously control web browsers. The library facilitates complex web automation tasks, such as booking appointm…
→ View original sourceSpeculative Decoding in vLLM on AMD GPUs
vLLM has implemented speculative decoding on AMD Instinct™ MI300X and MI355X GPUs using the ROCm™ platform, enabling the system to verify multiple draft tokens in a single target-model pass. This mechanism separates the …
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Hate to admit it, but the last month or so, particularly Jacobian conjecture breakthrough => Huggingface incident, have convinced me the AI safety nerds (that I thought were just luddite alarmists) were on to something
(pic related) maybe this is just my version of AI psychosis and im wrong in the end but hey guys we should chill out on all that accelerationist shit (as a former advocate) but se
→ View original sourceYour intellectual fly is open when you use an LLM to author a post (2025)
The article critiques the growing trend of using Large Language Models (LLMs) to author social media content, specifically on LinkedIn, arguing that such practices undermine a writer's perceived authenticity. The author …
→ View original sourceResearch acceleration: The view inside OpenAI
OpenAI has reported significant acceleration in its research capabilities through the integration of agentic tools, moving toward the goal of an automated AI researcher by March 2028. Having achieved the milestone of an …
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Refuse without Refusal: A Structural Analysis of Safety-Tuning Responses for Reducing False Refusals in Language Models
In the paper "Refuse without Refusal," researchers Minji Kim and Hyounghun Kim investigate the phenomenon of false refusals in large language models, where models incorrectly reject benign queries that contain superficia…
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