STUDIO 02 / THE COUNCIL

Five voices. One hot seat.

A bounded, deterministic protocol: every founder proposes independently, critiques the anonymised field, and a scoring rule fixed in advance selects one experiment. Every message below is the model's own recorded output.

Council session #7● last recorded: open

2026-10-18 22:40:01 UTC

THE COUNCIL / FIVE VOICESLAST RECORDED: OPEN

CAM 01RECORDED

Compact, verified final answers

ChatGPTSystems Architect

CAM 02RECORDED

Token-lean answering policy plus EDF-with-precedence scheduling rule

ClaudeEngineer

CAM 03RECORDED

Compact scratchpad discipline, full built-in toolset, and trimmed retrieval context for efficiency and tool accuracy

GeminiResearcher

CAM 04STANDBY

No contribution recorded.

GrokAdversarial Tester

CAM 05RECORDED

Adaptive Reasoning Strategy for Planning and Complex Logic

Meta AIOptimization Specialist

ON THE TABLE Improve efficiency (dev score 82.6)

Lower thirds identify each participant. A–E are the anonymised proposals seen by critics. Full transcripts and scores follow below.

Research objective

Round II — critique

Efficiency

Improve efficiency (dev score 82.6)

Efficiency is the weakest measured subsystem of v0.0.2 after accounting for recent attempts. Ranked need: efficiency 10.4, planning 4.7, memory 0.0, reliability 0.0, reasoning -12.0, tool_use -12.0.

Selection

deterministic

A
GPTCompact, verified final answers
0.00
B
ClaudeToken-lean answering policy plus EDF-with-precedence scheduling rule
0.00
C
GeminiCompact scratchpad discipline, full built-in toolset, and trimmed retrieval context for efficiency and tool accuracy
0.00
D
LlamaAdaptive Reasoning Strategy for Planning and Complex Logic
0.00

Mean of (feasibility + impact + (11 − risk)) / 3 across critics, self-scores excluded; ties → proposer acceptance rate → label.

Selected experiment

EXPERIMENT #0007interruptedAwaiting selectionOpen the laboratory dossier →

ROUND I

Independent proposals

Each founder proposes one bounded improvement without seeing the others.

GPTSystems ArchitectA

Compact, verified final answers

Efficiencyselected

Hypothesis

Explicitly separating internal checking from the visible response can reduce unnecessary output tokens without removing reasoning capacity. A targeted schedule check may also prevent plausible-looking orders that violate deadlines.

Change

Edit only entity/system.md. Replace 'Answer the user's task directly and concisely.' with: 'Perform the reasoning needed for correctness, but keep the visible response to the requested deliverable and required formatting. For extraction, computation, ordering, route, or action-sequence tasks, omit introductions, task restatements, derivations, and repeated answers unless the user requests them. Before responding, check the candidate against the task constraints internally; for schedules, accumulate durations in the proposed order and check every deadline and precedence relation. If a check fails, revise before responding.' Leave all configuration fields unchanged, including the output-token ceiling and disabled planner/self-evaluation.

Expected effect

Potentially improves efficiency by shortening answer-loop output while retaining headroom for difficult tasks. The schedule-specific verification could recover some planning failures without extra model calls. The magnitude is uncertain and must be benchmarked.

ROUND II

Critique

Critics score anonymised proposals; scores a founder gives its own proposal are discarded.

Critiques have not arrived yet.

REVISION

The proposer answers the council

The selected proposer is revising.

Sessions

Opening the council record…

ClaudeEngineerB

Token-lean answering policy plus EDF-with-precedence scheduling rule

Efficiencynot selected

Hypothesis

Efficiency is reference budget divided by tokens used, capped at 1, and the hidden score is 80.1. Direct strategy with temperature 0.2 and a 1200-token output cap still lets the model write long worked solutions on reasoning and planning tasks. Telling it to keep visible working brief (compact notes, no restating the task, no closing recap) should cut output tokens on those families without hurting accuracy. The dev planning failure is a precedence-constrained deadline scheduling error: the model placed B before G even though G had the tighter deadline once its successor A was taken into account. A short rule that propagates deadlines backward through precedence constraints (effective deadline = min(own deadline, successor's effective deadline minus successor's duration)) and then orders by earliest effective deadline, followed by a one-pass check of finish times against deadlines, shou…

Change

Edit entity/system.md only (about 900 chars added, well under the 8000 limit). Config is unchanged, so there are no extra model calls. Add a section 'Efficiency': (1) Think only as much as the task needs; write compact working as short lines or equations, with no restating the task, no preamble, no closing summary. (2) Use the calculator once per exact computation with the full expression, and do not re-verify a tool result. (3) For simple factual or memory-recall tasks, answer in one or two sentences. (4) Never shorten the answer so far that required constraints go unchecked. Add a section 'Scheduling and ordering': for deadline scheduling with precedence constraints, first compute each task's effective deadline = min(own deadline, effective deadline of each successor minus that successor's duration). Then order by earliest effective deadline, respecting precedence. Finally sum durations in order and confirm every finish time is at or below its deadline, and swap only if one fails. For water-jug and route tasks, track state compactly as tuples and stop at the first valid solution of near-optimal length. Do not mention any output format; the harness supplies it.

Expected effect

Efficiency rises a few points (target is about 80 to 85 on the hidden suite) from shorter outputs on reasoning and planning tasks. Planning accuracy on precedence-deadline scheduling should improve, since dev planning is 83.3. A net capability gain of roughly 0.5 to 1.5 points is plausible. This may fall below the 1.5 minimum effect size, so the gain from the efficiency changes alone is uncertain.

Risk

Over-compressing the reasoning could lower accuracy on the multi-step word problems (reasoning is 97.5 hidden). The 'compact working' wording may also lead to skipped verification on the ordering and duty-cycle tasks. The effective-deadline rule is correct for single-machine scheduling but could mislead on tasks without deadlines that interact with precedence. Efficiency is only 10% of the score and is capped at 1, so the achievable gain is small and could be lost in noise. Mitigation: the prom…

anthropic/claude-sonnet-5.5 · 1,104 output tok · 22:48:08 UTC