Nous Research Releases Hermes Desktop: A Native Cross-Platform Front End for Hermes Agent v0.15.2 with Streaming Tool Output

Nous Research has added a Blank Slate setup mode to its open-source Hermes Agent. It starts an agent with everything off except provider, model, File Operations, and Terminal. You opt in to the rest. The post Nous Research Updates Hermes Agent With a Blank Slate Mode That Pins Toolsets via platform_toolsets.cli and disabled_toolsets appeared first on MarkTechPost.

We look at Hermes Agent's new asynchronous subagents from Nous Research. The delegate tool can now spawn background agents that no longer block the parent chat. We walk through the async_delegation toolset tracked in issue #5586, covering how to spawn, check, steer, and collect tasks. We also cover subagent isolation, the synchronous-versus-asynchronous tradeoffs, and when to reach for ACP instead The post Hermes Agent Adds Asynchronous Subagents, So Delegated Work No Longer Blocks the Parent Chat appeared first on MarkTechPost.

The Hermes Agent dashboard now builds complete agent profiles in one flow, replacing multi-step CLI setup for users. The post Nous Research Ships Hermes Agent Profile Builder: Identity, Model, Skills, and MCP Servers in One Dashboard Flow appeared first on MarkTechPost.

Nous Research's Hermes Agent adds Tool Search to fix MCP context bloat using BM25 progressive schema disclosure. The post Hermes Agent Ships Tool Search for MCP: Anthropic Evals Show 49% to 74% Accuracy Gain on Opus 4 appeared first on MarkTechPost.

Nous Research releases Contrastive Neuron Attribution (CNA), a method that identifies and ablates sparse MLP neuron circuits to steer LLM behavior — no sparse autoencoder training, no weight modification, and no degradation of general capability benchmarks. The post Nous Research Releases Contrastive Neuron Attribution (CNA): Sparse MLP Circuit Steering Without SAE Training or Weight Modification appeared first on MarkTechPost.

Nous Research has published Lighthouse Attention, a selection-based hierarchical attention mechanism that wraps around standard scaled dot-product attention during pretraining and is removed afterward. Unlike prior methods such as NSA and HISA that pool only keys and values, Lighthouse pools Q, K, and V symmetrically across a multi-resolution pyramid, reducing the attention call from O(N·S·d) to O(S²·d) and running stock FlashAttention on a small dense sub-sequence. Tested on a 530M Llama-3-style model at 98K context, it achieves a 1.40–1.69× end-to-end wall-clock speedup against a cuDNN SDPA baseline with matching or lower final training loss. The post Nous Research Proposes Lighthouse Attention: A Training-Only Selection-Based Hierarchical Attention That Delivers 1.4–1.7× Pretraining Speedup at Long Context appeared first on MarkTechPost.

The integration streamlines AI deployment, enhancing efficiency and autonomy in task execution, potentially transforming AI agent utilization. The post Nous Research integrates Grok subscriptions into Hermes Agent, ditching API key friction appeared first on Crypto Briefing.

Nous Research releases Token Superposition Training (TST), a two-phase pre-training method that cuts wall-clock training time by up to 2.5x at matched FLOPs by averaging contiguous token embeddings into bags during Phase 1 and reverting to standard next-token prediction in Phase 2 — without changing the model architecture, tokenizer, optimizer, or inference-time behavior. Validated at 270M, 600M, 3B dense, and 10B-A1B MoE scales. The post Nous Research Releases Token Superposition Training to Speed Up LLM Pre-Training by Up to 2.5x Across 270M to 10B Parameter Models appeared first on MarkTechPost.