Machine Intelligence Research Institute

The work introduces RucTangle, an agentic commit-untangling method that groups a patch's changes by development purpose, orders them, applies each commit and checks whether the code still runs, revising order first, then regrouping via the agent, and finally merging adjacent commits while preserving the patch's final code; on 131 agent-generated patches it is the only method that reconstructs every patch with all commits runnable, at a median of eight commits, whereas baselines leave 20.6%–37.4% of histories with at least one unrunnable version. The authors also present TangleEval, which has two coding agents repair 453 regression-introducing patches; adding RucTangle histories plus git bisect results raises pass@1 from 26.9% to 31.3% and from 30.8% to 36.0%.

The work introduces RucTangle, an agentic commit-untangling method that groups a patch's changes by development purpose, orders them, applies each commit and checks whether the code still runs, revising order first, then regrouping via the agent, and finally merging adjacent commits while preserving the patch's final code; on 131 agent-generated patches it is the only method that reconstructs every patch with all commits runnable, at a median of eight commits, whereas baselines leave 20.6%–37.4% of histories with at least one unrunnable version. The authors also present TangleEval, which has two coding agents repair 453 regression-introducing patches; adding RucTangle histories plus git bisect results raises pass@1 from 26.9% to 31.3% and from 30.8% to 36.0%.

The work presents Wayfarer, a general, domain-agnostic, online deep RL agent that discovers options through Laplacian representation learning from high-dimensional observations and leverages them for control; the authors report that the resulting options simultaneously improve exploration, accelerate credit assignment, and generalise effectively to unseen settings, enabling substantially faster learning of complex policies and achieving state-of-the-art performance among single-stream agents on the most challenging Atari 2600 games, with the largest gains in games requiring long-horizon exploration and strategic behaviour, such as Montezuma's Revenge and Private Eye.

The work presents Wayfarer, a general, domain-agnostic, online deep RL agent that discovers options through Laplacian representation learning from high-dimensional observations and leverages them for control; the authors report that the resulting options simultaneously improve exploration, accelerate credit assignment, and generalise effectively to unseen settings, enabling substantially faster learning of complex policies and achieving state-of-the-art performance among single-stream agents on the most challenging Atari 2600 games, with the largest gains in games requiring long-horizon exploration and strategic behaviour, such as Montezuma's Revenge and Private Eye.