NextSense AI · Research

Notes from the workbench.

Research on how reasoning gets into a language model, and engineering notes on what breaks when a spoken agent is put in charge of a live runtime. Negative results stay in.

More writing

Engineering

Voice is a command surface, not a companion: what always-on gets wrong about coding agents

Full-duplex voice closed a conversational gap measured in milliseconds; long-running coding agents fail in a gap measured in minutes — which is why an always-on microphone is the wrong default for work that runs for four.

Engineering

Every guard needs an exit: liveness in agent guardrails

A guard that is perfectly safe can be fatally un-live: ours refused the same call nineteen times in a row, each refusal correct, and manufactured the exact failure it was built to prevent.

Engineering

Why your voice agent talks over itself

Over-talking is not a prompting failure — it is a concurrency problem wearing a UX costume, and no instruction to be more concise can fix a race for a single speaking channel.

Engineering

The agent is blind: grounding a spoken agent to a live canvas

The most recurring failure in our voice workstations was never a wording problem — an agent that co-drives a screen it cannot see will drift from it unless the pixels and the words are published by the same machine.

Release

The risk is one move ahead of the words

Forecasting where a conversation could go catches intent that the visible turn hides — but only if the look-ahead is probability-weighted: worst-branch aggregation flagged 87.5% of authorized defensive work, expected-peak flagged 37.5%, all of it soft.

Release

Why give a language model a tree?

On a frozen rated chess holdout, a bounded executable search around a fast model turned 1 correct answer out of 25 into 21 — and on AIME with tools, the same controller made things worse.

Research

The dataset is the asset

In conversation-safety tooling the durable artifact is not the classifier — it's the dataset contract: a sanitized risk taxonomy, benign hard-negatives curated as carefully as the risky cases, and an evaluation designed to embarrass you.

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