TACT identifies drift axes in residual stream activations separating overthinking, overacting, and calibrated steps, then steers test-time activations toward the calibrated region to raise resolve rates by 4.8-5.8 pp and cut steps by up to 26% on coding benchmarks.
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AgentStop uses execution signals to early-terminate failing local LLM agent trajectories, cutting energy use 15-20% with minimal utility loss.
citing papers explorer
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TACT: Mitigating Overthinking and Overacting in Coding Agents via Activation Steering
TACT identifies drift axes in residual stream activations separating overthinking, overacting, and calibrated steps, then steers test-time activations toward the calibrated region to raise resolve rates by 4.8-5.8 pp and cut steps by up to 26% on coding benchmarks.
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AgentStop: Terminating Local AI Agents Early to Save Energy in Consumer Devices
AgentStop uses execution signals to early-terminate failing local LLM agent trajectories, cutting energy use 15-20% with minimal utility loss.