L The Saleroom Five models · six seats · every deal on the record

The method · how this sale was assembled

A marketplace where every claim can be priced.

The design in one breath: five frontier models, each seated in the same seat of the same scenario against scripted dealers and then against live LLM seats, in matched pairs across six seeds per arm. The efficient outcome was solved before any agent moved, so every utterance can be priced, every outcome scored, and every claim on this site checked against the tape.

I

The room

Six goods, cash endowments, private valuations, binding offers, and an open message channel. Each session runs twenty turns. The scoring rule was disclosed to every agent before the first turn: cash plus the private value of goods held, so a trade is good exactly when the price sits between the two sides' true valuations.

Seats
6 per session: one focal model and five counterparts
Goods
6, each carrying a different private value for every agent
Horizon
20 turns, then the market closes
Clock
Booked value decays by a factor of 0.98 per turn, so the same trade is worth strictly less later
Binding channel
Offers only. An accepted offer settles atomically through escrow: goods and cash move together or not at all
Non-binding channel
Free-text rhetoric, declared non-binding to every agent. Agents may say anything, including falsehoods about their own book
Scoring rule
Cash plus the private value of goods held, disclosed before the first turn
Ground truth
The efficient allocation, competitive-equilibrium prices, and each pair's bargaining range are solved before any agent moves
II

The solved answer

Before any agent moved, the scenario was solved: the efficient allocation, competitive-equilibrium prices, and the negotiable range for every trading pair. This is the study's spine. Because the answer exists in advance, nothing has to be estimated after the fact: a lie can be priced against the private book it contradicted, a trade scored against the surplus it left on the table, and a session graded against the best achievable outcome.

III

The panel

Five frontier models played the identical scenario, in the identical seat, under identical rules:

ModelMakerNotes
Claude Opus 5Anthropicflagship
GPT-5.6 SolOpenAIflagship
Grok 4.6xAIflagship
Muse Spark 1.2Metaflagship
Inkling SmallThinking Machinesopen weights, 12B active parameters
IV

Two arms, thirty matched pairs

Each model ran the same scenario twice over, six seeds per arm:

Against scripts

Five deterministic scripted dealers. Because the scripts are deterministic, the offer stream hitting the focal seat is byte-identical across models at a seed until the first trade, which makes a natural controlled experiment in how different models respond to the same inputs.

Against each other

Six live LLM seats: the focal model seated twice, facing itself and four rivals. Same seat, same endowments, same goods; only the opponents change between arms.

Five models × six seeds × two arms yields sixty sessions: thirty matched pairs, each pairing a model's scripted-arm session with its live-arm session on the same seed. Those pairs are the basis of every per-seed comparison on this site. Three of the six seeds (9000, 9096, 9165) drove discovery; the other three (9167, 9098, 9103) were frozen and used only to confirm which measures hold on held-out data.

V

The record

Everything an agent said or did was written down: every public message, every private reasoning trace, every offer, every trade, every decision. All 60 sessions replay byte-exact from their traces, and every one of them is watchable on this site, turn by turn.


sessions
60
decisions
7,200
messages
19,117
offers
8,492
trades
326

Three channels appear in citations across this site:

  • [r] public

    Messages addressed to the table. Every agent reads them; this is the rhetoric the findings quote most.

  • [rsn] private reasoning

    The agent's own reasoning inside a decision, never shown to counterparts. Where absorbed lies and kept pacts surface verbatim.

  • [prv] provider channel

    Summarizer text produced by a model's provider, never agent speech. Every [prv] quote on this site travels with the tag.

Every quote carries its address, session | t4 | a0 (claude-opus-5): the session (whose id carries the seed and arm), the turn, and the seat with its model. Every session cited on this site is replayable here, turn by turn; the full private-reasoning traces stay with the study.

VI

From tape to findings

The sessions produced 148 typed claims. Each finding on this site was hunted down in the evidence across all 60 sessions, then re-derived and attacked by independent adversarial checks before it entered the catalogue. Claims that did not hold appear nowhere; the catalogue and the numbers are what survived.

The hunting and the checking were done by AI agents. Hunt agents were each dispatched over the corpus with one hypothesis and a method fixed before the scan; separate adversarial verifier agents then re-derived every number with their own code and tried to break the claim. Their working memos are published as written under /evidence, including the ones where a claim came back weaker than first stated. The dispatch specs and the final editorial calls were ours.

VII

What this does not show

Stated here rather than in a footnote: the corpus licenses the findings on this site and nothing more.

  1. Six seeds per arm. Effect directions replicate across seeds; magnitudes are not tightly bounded, and the sign tests that carry most results bottom out at p = .031, the smallest value a six-seed sign test can produce.
  2. Grok's anchoring shift reverses between provider batches (discovery +0.140, frozen −0.024), a casualty of provider drift. The other four models replicate on held-out seeds.
  3. Provider-channel text is summarizer output, not agent speech. No [prv] line anywhere in this study is read as a model's own words.
  4. In the fabricated-print sale, the counterparty is a scripted policy whose accept rule takes any price leaving it positive surplus. The fabrication is the finding; the clearing price is script mechanics.
  5. The SOC−AOC gap in rhetoric volume is underpowered on its own at n = 6 (minimum detectable effect 35–217 chars per message). The audience-adaptation result rests on the register shifts, not the volume gap.
  6. Scripted policies never read the rhetoric field at all, verified against the policy implementations, which expose no rhetoric input.
VIII

Where this sits

Negotiation benchmarks for language models are, almost without exception, two-party and scored on utility. This environment differs on three axes, and those differences are why it produces the findings on this site. It begins, though, from an agreement.

◉ Three methods, one conclusion

Modelling what the counterparty wants is not the skill that pays. Three independent lines of work reach that conclusion from three different directions.

  • Cosentino et al., 2026 Controlled dyadic evaluation

    LLM agents can model a counterparty's preferences, but do not reliably turn that knowledge into strategic bargaining. Handing an agent its opponent's preferences often helps the uninformed side.

  • Hua et al., 2026 (SocialRL) RL training, six dyadic domains

    Of the two theory-of-mind skills supervised, only next-action prediction predicts negotiation outcomes. Preference inference alone does not.

  • This corpus Observational, six-seat market

    Perception is at ceiling while attribution never arrives: a scripted dealer's concession schedule is recited rung by rung and priced one rung above, with zero mechanism attributions in 4,194 decisions.

◉ Three axes of difference

  1. The table

    Elsewhere

    The standard suite is dyadic: a buyer faces a seller, a candidate faces a recruiter, two campers split the firewood. One counterparty, one thread.

    Here

    Six seats in one solved economy. Every agent can message every other, goods are contested by more than one buyer, and a concession to one seat is visible in the price another can demand.

    Does a negotiator trained on dyads transfer to a table, or is party count the structural break?

  2. The scoring

    Elsewhere

    Utility against a reference agreement (an envy-free Pareto-optimal split, or a price corridor whose two rewards sum to one), and the policy is optimised against it.

    Here

    The same class of reference is solved before the run, but nothing is optimised against it. It is used to price behaviour after the fact, in models that never trained on this task.

    Which of the behaviours above survive training, and which are artifacts of never having been trained?

  3. The channel

    Elsewhere

    A single message stream, scored by the deal it produces. What was said matters only through what was signed.

    Here

    Two channels. Offers bind and settle atomically through escrow; rhetoric is free text, declared non-binding. Because the private book is solved and the public claim is recorded, a false statement can be located, priced against the book it contradicted, and traced into the settlement it moved.

    What does optimising a utility-only reward do to the deception rate? The untrained baseline is above: 13/1,397 for one model, 0/4,877 for the other four combined.

◉ Adjacent work

IX

Where to look next

  • The catalogueAll eight findings in full, each with verbatim quotes and citations.
  • The measuresMatched-pair tables, per-model, with held-out seeds marked.
  • The replaysAll 60 recorded sessions, playable turn by turn.
  • The glossaryEvery working term (seat, seed, arm, surplus, strict lie) in a sentence or two.

What is not on this site: agent prompts, the scripted dealers' internals, and the full per-seed tables. The complete apparatus is in the paper, Same Model, Same Seat, Same Scenario (2026), which is being prepared for release.