One-Line Summary

Smaller agentic models push ahead by planning over longer sequences, while test-time world models and interactive coding benchmarks reshape how we evaluate agent performance.

Research Papers

35B agent approaches trillion-parameter performance by extending its horizon

This paper presents an AI agent that tackles complex, multi-step tasks by planning much farther ahead instead of simply adding more parameters. The team introduces Agents-A1, a35 billion-parameter Mixture of Experts ** (MoE) agent that targets long-horizon problems with very long multi-step trajectories. It reports average agentic trajectories of about45K tokens**, aiming to reach the results of trillion-parameter systems on long-horizon benchmarks. ¹

To make long plans usable, the authors build a knowledge–action infrastructure that links external knowledge, actions, observations, and verifier outcomes into long trajectories. Training follows three stages: full-domain **supervised fine-tuning ** (SFT) to align base agent behaviors, domain-level teacher models to capture specialized expertise, and a multi-teacher, domain-routed on-policy distillation with salient vocabulary alignment that merges six heterogeneous domains into one deployable student. ¹

On long-horizon agent benchmarks, Agents-A1 reports leading or competitive results versus trillion-parameter systems like Kimi‑K2.6 and DeepSeek‑V4‑pro: SEAL‑0 56.4,IFBench 80.6,HiPhO 46.4,FrontierScience‑Olympiad 79.0,MolBench‑Bind 56.8, with competitive scores onSciCode 44.3,HLE 47.6, andBrowseComp 75.5. The authors position horizon scaling as a practical path for a 35B agent to reach or match 1T-level performance on long-horizon tasks. ¹

WorldEvolver: test-time memory helps agents plan better

WorldEvolver introduces a “world model” that updates what it remembers during deployment to make an agent’s lookahead more reliable—without changing the downstream agent or any model weights. In context, a **large language model ** (LLM) agent can use three modules: Episodic Memory (retrieval-based simulation from real transitions), Semantic Memory (heuristics extracted from prediction–observation mismatches), and Selective Foresight (filtering low-confidence predictions before they enter the reasoning context). ²

Evaluated on ALFWorld and ScienceWorld, the framework measures world-model prediction accuracy on Word2World and downstream agent success on AgentBoard. Experiments report the highest prediction accuracy across three backbones and stronger downstream success rates than other world-model baselines, indicating that test-time memory revision improves both foresight quality and planning outcomes. ²

SWE-Together: coding agents evaluated in real multi-turn sessions

SWE‑Together reconstructs real user–agent coding interactions so agents are judged as collaborators, not just by final code snapshots. The authors curate 109 repository-level tasks from11,260 recorded sessions, then replay them with a reactive**Large Language Model ** (LLM)–based user simulator that preserves original user intent and intervenes when the agent’s progress requires it. ³

The benchmark evaluates both final repository correctness and how many corrective feedback turns an agent needs. Results with frontier coding agents show a clear pattern: stronger agents tend to achieve higher final success while needing fewer interventions—evidence that multi-turn collaboration quality can be measured, not assumed. ³

Open Source & Repos

Goose: desktop and CLI agent that installs, runs, edits, and tests code

Goose is an extensible open-source agent with a desktop app, a command-line interface (CLI), and an application programming interface (API) that can install dependencies, execute programs, edit files, and run tests with any**Large Language Model ** (LLM)—released under the Apache‑2.0 license. The project has moved from block/goose to the Agentic AI Foundation at the Linux Foundation. ⁴

The latest release, **v1.39.0 ** (2026-06-25), highlights features for handling “recipes” and managing global configuration and session extensions—useful for packaging repeatable agent workflows. ⁴

Why It Matters

Long-horizon results from a 35B MoE agent suggest a shift from “more parameters” to “more horizon”—that is, investing in longer, verified trajectories and multi-domain distillation to teach agents how to plan and act over many steps. ¹

At the same time, evaluation is moving toward reliability and interaction: test-time memory aims to make foresight trustworthy, and multi-turn coding benchmarks measure collaboration rather than single-shot accuracy. Together, these trends point to agent systems, memory, and measurement as the levers that matter most. ²

What to Try This Week

1. Goose desktop/CLI: install from GitHub and run a starter “recipe” to automate a small code task. https://github.com/aaif-goose/goose