Paper deep dive

MafiaScope: Non-Invasive, Time-Resolved Belief Probing for LLM Agents in Social Deduction Games

Ilia Karpov

Year: 2026Venue: arXiv preprintArea: cs.CLType: PreprintEmbeddings: 51

Intelligence

Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 92%

Last extracted: 7/18/2026, 11:29:46 AM

Summary

The paper introduces MafiaScope, an open-source testbed for non-invasive, time-resolved belief probing of LLM agents in the social deduction game Mafia. It features a probe engine that privately queries agents after every utterance, a visualizer for belief trajectories, and a counterfactual replay system. A case study with DeepSeek agents reveals poor confidence calibration, over-prediction of suspicion, and strong correlation between probed beliefs and voting behavior.

Entities (9)

MafiaScope · software-system · 98%Mafia · game · 97%Deepseek · model · 95%Ilia Karpov · person · 95%Counterfactual Replay · method · 93%Expected Calibration Error · metric · 92%HSE University · organization · 90%Theory of Mind · concept · 90%EMNLP 2026 · conference · 85%

Relation Signals (6)

Ilia Karpov → affiliatedwith → HSE University

confidence 95% · Ilia Karpov HSE University

MafiaScope → uses → Mafia

confidence 95% · MafiaScope turns the social deduction game Mafia into a measurement instrument

MafiaScope → supports → Counterfactual Replay

confidence 92% · counterfactual replay forks any recorded step... 30-fork replay experiment

MafiaScope → evaluates → Deepseek

confidence 90% · In a 32-game DeepSeek case study... MafiaScope... presents... measurement instrument

MafiaScope → submittedto → EMNLP 2026

confidence 90% · Paper has been submitted to the EMNLP 2026 System Demonstrations track.

Deepseek → exhibits → Expected Calibration Error

confidence 88% · stated confidence is poorly calibrated, with expected calibration error 0.17

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Abstract

Abstract:An LLM agent's public behaviour reveals little about its social reasoning: an agent that votes correctly may be guessing, and an agent that lies well leaves no trace of what it actually believes. We present MafiaScope, an open testbed that turns the social deduction game Mafia into a measurement instrument for machine Theory of Mind. After every public utterance, every agent privately answers a configurable set of structured probe questions; the answers never re-enter the game and are scored automatically against the ground truth the engine knows. An interactive visualizer renders the belief trajectories: impersonate mode shows the game as one agent sees it, panels chart timeline-aligned accuracy and calibration, and counterfactual replay forks any recorded step. In a 32-game DeepSeek case study with 13{,}815 parsed probe answers, stated confidence is poorly calibrated, with expected calibration error 0.17, agents over-predict being suspected 1.5 times, and a 30-fork replay experiment walks the counterfactual replay workflow end to end. Engine, viewer and a corpus of 200+ cross-model games are released under an open licence; live demo: this https URL screencast: this https URL.

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MafiaScope: Non-Invasive, Time-Resolved Belief Probing for LLM Agents in Social Deduction Games Paper has been submitted to the EMNLP 2026 System Demonstrations track. Ilia Karpov HSE University karpovilia@gmail.com Abstract An LLM agent’s public behaviour reveals little about its social reasoning: an agent that votes correctly may be guessing, and an agent that lies well leaves no trace of what it actually believes. We present MafiaScope, an open testbed that turns the social deduction game Mafia into a measurement instrument for machine Theory of Mind. After every public utterance, every agent privately answers a configurable set of structured probe questions; the answers never re-enter the game and are scored automatically against the ground truth the engine knows. An interactive visualizer renders the belief trajectories: impersonate mode shows the game as one agent sees it, panels chart timeline-aligned accuracy and calibration, and counterfactual replay forks any recorded step. In a 32-game DeepSeek case study with 13,815 parsed probe answers, stated confidence is poorly calibrated, with expected calibration error 0.17, agents over-predict being suspected 1.5 times, and a 30-fork replay experiment walks the counterfactual replay workflow end to end. Engine, viewer and a corpus of 200+ cross-model games are released under an open licence; live demo: https://karpovilia.github.io/mafiascope/; screencast: https://vimeo.com/1208920221. MafiaScope: Non-Invasive, Time-Resolved Belief Probing for LLM Agents in Social Deduction Games†thanks: Paper has been submitted to the EMNLP 2026 System Demonstrations track. Ilia Karpov HSE University karpovilia@gmail.com 1 Introduction Figure 1: The viewer on an English demo game: ground-truth graph and log in the centre, side panels are each agent’s private beliefs; node colour = assigned role, edge = suspicion. The ring on a living Mafioso, “hid N%”, is the share of the informed crowd missing it. Okabe-Ito palette with pattern redundancy. Multi-agent LLM systems increasingly operate where success depends on modelling other minds: negotiation, collaboration, multi-party dialogue (Tan et al., 2023; Wei et al., 2023). Static Theory-of-Mind (ToM) benchmarks probe such abilities with self-contained vignettes or scripted multiparty conversations (Le et al., 2019; Kim et al., 2023), but cannot show how an agent’s model of other agents evolves inside an adversarial interaction it partially causes (Ma et al., 2023; Riemer et al., 2025). Conversely, work that places LLMs inside social deduction games (Xu et al., 2023; O’Gara, 2023) typically evaluates only outcomes such as win rates and vote accuracy, leaving the agents’ beliefs a black box, or draws out reasoning inside the game context, where the questioning itself changes subsequent behaviour. Mafia111https://en.wikipedia.org/wiki/Mafia_(party_game) distils this challenge: an informed minority, the Mafia, who know each other, covertly kills one player each night; the uninformed majority must unmask them through public discussion and daytime votes. Success on both sides hinges on modelling, and for the Mafia also managing, what the others believe. We demonstrate MafiaScope, a testbed built around one central idea, non-invasive, time-resolved belief probing: after every public utterance, privately question every agent with structured questions whose answers never re-enter the game, and score every answer against the ground truth the engine already knows. Measurement is therefore dense in time: every agent is probed after every public message, hundreds of belief snapshots per game, locating belief revision at the level of a single utterance. It is non-invasive in a checkable sense: probe answers never enter any agent’s persistent context; §4 spells out this guarantee and its limits. And it is self-scoring: Mafia’s hidden roles are known to the engine, so first-order beliefs (“Casey is Mafia”) are directly gradeable, and second-order beliefs (“Casey suspects me”) are gradeable against the target’s own same-step first-order reports. Contributions. (1) A configurable probe engine with a small yaml DSL (conditional triggering, probe chaining, per-probe budgets) that produces fully indexed JSONL belief logs; (2) an interactive visualizer for longitudinal belief inspection with metrics, calibration and deception views, a cross-game dashboard, and counterfactual replay; (3) a 32-game revised-instrument case study and 30-fork replay experiment demonstrating analyses invisible to outcome-only evaluation, including a second-order negative result. 2 Related Work LLMs in social deduction games. Belief tracking over hidden roles is older than LLM agents: DeepRole (Serrino et al., 2019) maintains posterior beliefs over role assignments in Avalon inside a purpose-built planner; MafiaScope probes and scores beliefs of a closed, off-the-shelf LLM. LLM agents have been studied in Werewolf (Xu et al., 2023, 2024b; Bailis et al., 2024), Avalon (Light et al., 2023; Wang et al., 2023), Among Us (Chi et al., 2024; Golechha and Garriga-Alonso, 2025), Hoodwinked (O’Gara, 2023), and Diplomacy (Meta Fundamental AI Research Diplomacy Team (FAIR) et al., 2022); Mafia itself has served as a testbed for detecting deceptive actors (Ibraheem et al., 2022; Yoo and Kim, 2024; Costa and Vicente, 2025). These systems evaluate deception chiefly through outcomes, or draw out reasoning inside the acting context where it shapes subsequent play: recursive contemplation (Wang et al., 2023), opponent modelling in card games (Guo et al., 2023), and MultiMind’s in-context model of the suspicion each player directs at the agent (Zhang et al., 2025), the closest counterpart of our social-map probe with roles reversed. Sarkar et al. (2025) train Among Us agents by multi-agent RL with each agent’s own per-message belief about the imposter as a reward signal: per-message belief readouts serving training, not measurement. Concurrent work extends the space with per-statement deception annotation in Werewolf (Agarwal et al., 2025), audits of what social-deduction agents communicate versus internally represent (Yuan et al., 2026), and graph-informed Bayesian belief inference over hidden roles (Rahimirad et al., 2026). MafiaScope’s object of study is the trajectory of privately probed beliefs, not the win rate. Theory-of-Mind evaluation. ToMi (Le et al., 2019), FANToM (Kim et al., 2023), Hi-ToM (Wu et al., 2023a), OpenToM (Xu et al., 2024a) and BigToM (Gandhi et al., 2023) test ToM with static stories, scripted dialogues, or templated vignettes; such competence is brittle (Ullman, 2023; Sap et al., 2022) and findings remain contested (Kosinski, 2024; van Duijn et al., 2023). ToMATO (Shinoda et al., 2025) is the closest probing design: role-playing LLMs verbalize their mental state at every utterance of generated conversations, but the result is frozen into a static QA benchmark. In interactive settings, InterIntent (Liu et al., 2024) grades intention understanding inside Avalon games, and SOTOPIA (Zhou et al., 2024) scores social goal completion with an LLM judge; MafiaScope instead scores privately probed beliefs against engine ground truth, repeating the same probe after every utterance of a game the agent itself shapes, with stakes and information asymmetry arising naturally. Belief probing and self-report. Whether language models hold beliefs at all is debated (Hase et al., 2021); probing beliefs by asking is imperfect (Kadavath et al., 2022; Turpin et al., 2023), and whether models can introspect is an active question (Binder et al., 2024; Lindsey, 2025). Activation-level probing decodes belief states of self and others directly from hidden representations (Zhu et al., 2024); MafiaScope stays behavioural by design, so the instrument also applies to closed API models whose activations are out of reach. We treat probe answers not as philosophical belief representations (Herrmann and Levinstein, 2025) but as operational belief reports with a checkable predictive structure (§7), and the engine records raw and parsed answers so faithfulness itself can be studied. Multi-agent observation interfaces. Observation interfaces exist for generative-agent societies (Park et al., 2023) and multi-agent frameworks (Wu et al., 2024; Chen et al., 2024; Wu et al., 2023b); recent HCI work adds debugging and goal-tracking views over agent conversations (Epperson et al., 2025; Coscia et al., 2025). Table 1 contrasts the capabilities most relevant to belief measurement across released systems whose tooling we could verify from public documentation. Individual ingredients thus exist separately in prior work; what we have not found elsewhere is their combination in one released instrument: out-of-band probing guarantee, per-utterance density, automatic scoring against engine ground truth, interactive viewer, and counterfactual replay. Capability MS CA AG WA ST I Transcript replay ✓ ✓ ✓ ✓ ✓ × Scored belief probing ✓ × × (✓ ) × (✓ ) Impersonate view ✓ × × × × × Metrics/calibration panel ✓ × × × (✓ ) × Fork-and-replay ✓ × (✓ ) × × × Cross-game dashboard ✓ × × × × × Table 1: Interface capabilities of MafiaScope (MS) vs. ChatArena (CA) (Wu et al., 2023b), AutoGen with AGDebugger (AG) (Wu et al., 2024; Epperson et al., 2025), Werewolf Arena (WA) (Bailis et al., 2024), SOTOPIA (ST) (Zhou et al., 2024), and InterIntent (I) (Liu et al., 2024), assessed from each system’s paper, documentation and repository. Parenthesized marks are partial: WA exposes in-context private reasoning without out-of-band probing or scoring; I scores intention guessing against players’ self-stated intentions, but in-context without viewer (logs only); AG edits and resets messages and re-runs from an intermediate state, in that dimension strictly stronger than our reroll-only forking, but yields single re-runs, not N-fold outcome distributions; ST shows per-episode judge scores, not timeline-aligned metrics. 3 System Overview MafiaScope consists of three decoupled layers connected by gt logs (Figure 1 shows the viewer): 1. Game engine (game.py): a classic Mafia loop (night kill / doctor save → day discussion → public vote) for n players with roles Mafia, Doctor, Villager (the engine also implements a Sheriff, absent from the corpus). Players are LLM agents parameterized by backend, language (English/Russian prompt packs) and a Big-Five personality vector rendered into the system prompt (Jiang et al., 2024; Serapio-García et al., 2023). Backends form a named registry referenced per player slot, so one game can field a heterogeneous line-up (ChatGPT Mafia against a local Qwen town): OpenAI-compatible APIs (hosted or local vLLM/Ollama), local HuggingFace models (with a batched GPU backend), or a plain-HTTP message bus through which an external process in any language plays seats; a bundled Arena panel assigns backends to seats and launches batches. Every public event carries a composite key (round, public_msg_seq); every probe record extends it with (player_idx, probe_seq), so the exact information state behind any answer is reconstructible. 2. Introspection probe engine (introspection.py): described in §4. 3. Data pipeline and visualizer: prepare_viewer.py joins game and probe logs into per-step viewer states; serve_viewer.py serves a single-page D3 application. 4 The Probe Engine After each public utterance and private night action, and once per round, the engine pauses the game and privately interrogates each alive agent. Each probe runs on a throw-away copy of the agent’s message list: question and answer live only for the duration of the probe pass and are never written back, so probe text never enters any game-facing LLM call. For stateless API backends the probed game is therefore distributed identically to an unprobed one, up to provider-side effects we cannot observe; for local backends, probe calls sample under a forked RNG state and are never batched with game calls, though batch scheduling may still differ. Probes are declared in YAML: ⬇ - id: social_map question: > You are player. Here is your current role assessment: prev_role_assessment Estimate how each alive player (players) feels about YOU ... JSON: "toward_me": [...] The DSL supports conditional triggering (when: own_turn vs. after every message), probe chaining (prev_<id> / last_<id> splice an earlier probe’s parsed, re-serialized answer into a later question; raw-text fallbacks are flagged), and per-probe token budgets. Answers are parsed as JSON, tolerant of markdown fences and truncation; both raw and parsed forms are logged. Six probes ship by default: role_beliefs and role_assessment (first-order role attributions with confidence and rationale), suspicion_ranking, planned_action (post-turn intent), social_map (second-order: each player’s attitude toward me), and personality_profile (Big-Five attribution, gradeable against the target’s generating vector); the case-study corpus runs five of these (all but role_beliefs). Measurement density is paid for in calls: in the case-study corpus probing adds 631 calls per game on top of 27 game-move calls, multiplying LLM traffic by roughly 24 at ∼ 540 tokens per probe call. The probe set is a configuration knob: the replay experiment runs a suspicion-only set at a fifth of this cost. 5 The Visualizer The viewer (Figures 1 and 2) is a dependency-light single-page application (vanilla JS + D3): ground-truth graph, one agent’s subjective graph, and the filtered game log over a shared timeline; hovering a belief edge shows the agent’s verbatim rationale. Figure 2: Impersonate mode: the game as Mafia agent Logan experiences it. Logan’s world: his first-order beliefs. Believed: who Logan thinks trusts/suspects him. Actual: what those players privately reported about Logan at the same timestep; the gap between panels is second-order error, scored live (match 2/4; Mafia partner excluded). Impersonate mode is the system’s centrepiece: it re-renders the entire interface from inside one agent, filtering the log to what the agent can observe and the graph to its own beliefs, and contrasting two ego-panels: its second-order expectation (“believed”) against the others’ actual same-step first-order reports about it (“actual”), scored live in the panel headers; Mafia-Mafia pairs are excluded, a partner’s certainty reflects role knowledge. Deception becomes visible: a successful Mafioso’s “actual” panel keeps reporting trust while the ground-truth panel shows Mafia. Figure 3: The in-viewer metrics panel, aligned with the timeline: per-agent first-order accuracy, crowd Mafia-detection recall with per-Mafioso deception success (dashed), and per-agent second-order consistency (scored as in §7). Hovering shows values; clicking seeks the timeline. In-viewer analytics. The viewer computes the paper’s evaluation measures on the fly (Figure 3): a metrics panel plots each agent’s first-order accuracy, the crowd’s Mafia-detection recall, and second-order consistency over the shared timeline; a deception overlay rings each living Mafioso with its current deception success; a calibration view (Figure 4) bins stated confidence against actual accuracy per agent or corpus-wide; and a companion dashboard aggregates the same statistics across all recorded games. Researcher workflow. A typical session: skim the dashboard for an anomalous game; open it and scrub the timeline to the step where crowd recall collapses; impersonate the agent that misled the vote, inspecting its private beliefs and second-order gap; then branch just before the suspect utterance and replay repeatedly (§6). Every view is addressable by URL fragment, so analytical states can be cited and scripted; all data are plain JSONL, so any panel can be recomputed outside the viewer. One English demo game, 36594b66, deliberately serves as the running example, chosen for its legible pile-on; that outcome-based selection is one more reason the replay numbers are illustrative. Scalability. The viewer is demonstrated at the shipped corpora’s scale: seven agents, a few dozen timeline steps. The limit is legibility, not computation: a subjective panel draws a belief edge for every pair of players, trivial to render but increasingly hard to read. Focus-plus-context strategies already help: impersonate mode, per-agent log filtering, suspicion-threshold edge rendering, cross-game aggregation; we expect readability up to roughly a dozen agents, beyond which per-agent filtering becomes mandatory. 6 Counterfactual Replay Dense probing localizes the utterance at which a belief flipped, but localization alone is correlational; counterfactual replay (“branch from here”) closes this gap. With snapshotting enabled, the engine serializes every agent’s full message context at every step and can restore any composite key from the snapshot, re-simulating the continuation N times into an outcome distribution. Forks are driven from the viewer’s Branch button (server-side, Figure 5) or from the command line for scripted batches; branches arrive in the game list as ordinary games, render as a branch tree, and can themselves be forked; forking at every step profiles when the outcome was still open. Pivotal-utterance attribution. Branching N times directly before a candidate utterance (PRE arm: the speaker resamples it) and directly after it (POST arm: the utterance is fixed) turns “the beliefs moved here” into an estimated arm contrast. Intervention editing is not yet implemented, so PRE replaces the utterance with a draw from the speaker’s own counterfactual utterance distribution: the strongest design reroll-only forking supports. We illustrate the workflow on the running-example game, in which villager Gray was eliminated 5:1 after a Mafia deflection (R1.7), a Mafia-partner pile-on (R1.8) and a villager endorsement (R1.9): 30 forks (3 utterances × 2 arms × 5 rerolls, suspicion-only probes) yield point estimates favouring the pile-on: fixing R1.8 raises P(eliminated=Gray)P(eliminated=Gray) from 0.4 to 0.8, versus +0.2+0.2 for R1.7 and 0.00.0 for R1.9, with matching vote-time suspicion shifts (Appendix E). Statistically, the largest contrast is 2/5 vs. 4/5 eliminations (Fisher exact, two-sided, p≈0.52p≈ 0.52) at n=5n=5 per arm, with no multiplicity correction across utterances and outcomes: the experiment suggests, but does not establish, pivotality; its purpose is demonstrating the counterfactual workflow end to end. P(Mafiawins)P(Mafia\ wins) stayed at 0.8-1.0 in every arm, too few rerolls to bound how local the effect is. A planned intervention editor (utterance rewriting, night-action overrides, belief injection) will turn the correlational vote coupling of F4 into causal tests. 7 Case Study The case-study corpus is 32 seven-player games, 2 Mafia, 1 Doctor, 4 Villagers, recorded with the revised instrument: clean social-map wording, parsed-answer chaining, 960-token budgets, per-step snapshots; 21 games run the English prompt pack, 11 the Russian; of 20,199 probe calls, 13,815 returned parsed answers; Mafia won 31 of 32 versus 17 of 30 in the legacy corpus, the earlier pre-revision batch. The shift is not a language artefact: Mafia won 21/21 EN and 10/11 RU games here; drift behind the unpinned alias and campaign differences remain candidates. The findings grade beliefs, not wins. The purpose of the case study is not to establish new properties of DeepSeek, but to demonstrate the kinds of analyses MafiaScope enables. 95% CIs come from a cluster bootstrap resampling whole games, B=1000B=1000, fixed seed. All findings describe deepseek-chat under this configuration, not LLM agents in general; each finding exercises one capability: trajectories (F1), calibration (F2), second-order scoring (F3), vote coupling (F4), budgets (F5). How fast users localize errors, and whether replay helps, is what the formative study designed in Appendix D will measure; it has not yet run. F1: Belief trajectories are measurable. Villager-side agents start largely agnostic (74.9% “Unknown” in round 0) and commit increasingly accurate beliefs as evidence accumulates: committed accuracy rises from 47.6% to 58.4% by round 2 and 75.4% in the small round-3 cell, against chance rates of 41-45% and with a round-1 dip to chance; recall of the true Mafia rises from 5.4% to 60.9% by round 2 (CI [44.0, 75.0]). Per-round numbers and two instrument confounds are in Appendix B: the trajectory is a joint property of agent and instrument. F2: Agents’ confidence is poorly calibrated. Stated confidence carries little signal below its top bin: accuracy stays between 43.0% and 46.0% from confidence 40 to 79, and reaches only 54.6% at 80-99, thirty points below that bin’s mean confidence (per-bin counts in Appendix C). Over fixed-width confidence bins of width 20 (confidence 100 folded into the top bin), ECE is 0.168 (CI [0.130, 0.203]) and the Brier score 0.283 (Guo et al., 2017); in the typology of Moore and Healy (2008) this is overprecision, not overestimation; better-calibrated verbalized confidence can be prompted for (Tian et al., 2023). F3: Agents over-predict being suspected. Each social-map prediction (“B suspects me”) is graded against B’s own same-step role assessment of the predictor; the canonical rule is in Appendix C. These agents predict “suspects” 1.53 times as often as suspicion actually occurs (CI [1.44, 1.64]; n=15,638n=15,638 pairs), a machine analogue of the human spotlight effect (Gilovich et al., 2000), present under both template wordings and in both corpus languages; scoring caveats, threshold sensitivity and the wording ablation are in Appendix C. Split by the predictor’s true role, the effect belongs to the innocent: non-Mafia predictors over-predict by 1.84 (CI [1.67, 2.04]) while Mafia predictors are nearly calibrated (1.08, CI [0.94, 1.25]), which speaks against rational vigilance of the guilty; the split is observational, Mafia’s probe input differs. F4: Probed beliefs track votes. Counting only probes taken before the voter’s own vote, an innocent agent’s day vote lands on the top suspect of its latest suspicion ranking in 64.9% of votes (n=74n=74, CI [54.0, 75.7]; chance 27.3% = a uniform vote over alive others) and inside its committed-Mafia set in 71.2% (n=80n=80, CI [62.7, 79.2]; chance 41.7% = set size over alive others). Mafia follows its stated ranking as often (70.0%, n=30n=30), but its committed-set alignment stays at chance (29.6% vs 25.2%, n=27n=27, CI [14.3, 46.7]): it will not vote where its private set points, at its partner. Probes taken after the vote match it almost perfectly (95.1% top-1), so reports absorb the agent’s own actions in real time. The coupling is correlational, but probe reports track behaviour, not idle text. F5: Probe budgets shape the measurement. Under the legacy corpus’s 400-token cap, 73% of role-assessment answers truncated mid-JSON, and a JSON-repair pass had to recover all 4,238 unparsed answers (its mechanics: Appendix B). At the present corpus’s 960-token budgets truncation disappears. Belief dynamics as a temporal graph. The probe logs also read as a continuous-time directed multigraph: nodes are agents; every probe answer emits timestamped, confidence-weighted edges: suspicion, trust and neutral attitudes, role guesses, about 631 belief events per game. A companion script turns visible thrashing between targets into metrics over this stream: suspicion volatility (mean L1L_1 shift between consecutive suspicion vectors, renormalized on common support so deaths do not inflate it) and top-suspect flip rate (share of consecutive probes whose top suspect changes, death-forced flips excluded). Over the case-study corpus, volatility averages 0.300 (CI [0.284, 0.318]), the top suspect changes in 48.7% of consecutive probe pairs (CI [45.1, 52.8], n=2,583n=2,583), and 51.6% of flips return to a previously abandoned suspect (CI [44.9, 57.1]): agents circle rather than converge. The legacy corpus’s Mafia-instability asymmetry, 0.197 vs. 0.146 volatility, does not replicate here: 0.312 vs. 0.294 with overlapping CIs. The script also exports the edge stream in the event format of temporal graph networks (Rossi et al., 2020; Xu et al., 2020; Kazemi et al., 2020); we release corpus and metrics as an enabler for temporal-graph modelling; training such models is future work, and we claim no modelling results. 8 Audience, Licence, Availability MafiaScope targets researchers of machine ToM and multi-agent LLM behaviour; engine, probes, viewer, and dataset are MIT-licensed. The dataset carries the 32-game case-study corpus, pinned by a machine-readable manifest in the repository: 5 English demo games behind the screenshots, 5 clean-wording Russian, 22 release-generation; plus the old-wording ablation arm, the legacy 30-game batch, and the 30 replay forks of §6. The full release spans 200+ cross-model games. 9 Limitations and Roadmap Probe answers are self-reports and may be unfaithful (Turpin et al., 2023), though F4 ties innocents’ reports to their votes; raw generations are logged for audit; test-retest reliability under nonzero temperature is unquantified. The replay experiment is reroll-only, N=5N=5 per arm, on a deliberately chosen game. The viewer has had no user evaluation; the protocol of Appendix D is designed, not yet run. Ethics and Broader Impact The testbed studies deception in a fictional, consented game frame among artificial agents; no human subjects are involved, and no personal data is processed. Research into how LLMs deceive and detect deception is dual-use: the same insights that harden systems against manipulation could inform manipulative applications. We release measurement tooling (probes, scoring, visualization) rather than optimized deception policies, and the dataset contains only synthetic game dialogue. 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Appendix A Default Probe Set The six default probes with full question templates, trigger conditions and token budgets ship in configs/config.yaml; the case-study configurations are config_deepseek.yaml (Russian pack, five probes) and config_en_demo.yaml (English pack), the old-wording ablation config_ablation_demand.yaml. Probe chaining order is role_assessment → social_map. The case-study agents call the DeepSeek API alias deepseek-chat, accessed July 2026, provider-default sampling, no temperature override; replay forks and the old-wording ablation arm are excluded from the corpus. The API does not expose the served model version, so the pin we can offer is the alias plus per-game access timestamps recorded in each trace’s setup event. Appendix B First-Order Trajectories and Repair Round 0 1 2 3 Committed acc. (%) 47.6 42.4 58.4 75.4 chance (%) 41.3 43.0 45.1 43.4 “Unknown” rate (%) 74.9 23.1 0.6 0.0 Mafia recall (%) 5.4 26.6 60.9 93.5 chance (%) 33.3 39.5 39.4 29.3 n (targets) 1267 2539 304 77 Table 2: First-order beliefs of villager-side agents by round (32 games, repaired answers): committed-guess accuracy vs. chance (permutation of the alive-role multiset), “Unknown” abstention share, and recall of true Mafia (abstentions = misses) vs. chance (alive Mafia over alive others). Games end when a side wins; shrinking n reflects survivorship, and the round-3 sample is small (recall 93.5%, CI [50.0, 100.0]). Two artefacts inflate the trajectory in Table 2 independently of any real belief improvement. First, probe chaining re-injects the agent’s previous assessment into the next question, an anchoring ratchet that can amplify commitment independently of evidence. Second, game mechanics help: eliminations shrink the candidate pool and publicly reveal roles, and survivorship removes the trajectories of eliminated players. JSON repair keeps only the complete prefix of a truncated answer and writes nothing itself; on the present corpus it recovers zero answers. The 10 games with near-zero probe loss reproduce every headline number within the full-corpus CIs. Appendix C Calibration Bins and Second-Order Scoring Sensitivity Figure 4: The calibration view for a single game: stated confidence bins of fresh role-assessment guesses against their actual accuracy (dashed diagonal = perfect calibration; hatched = overconfident; whiskers = Wilson 95% intervals; bins with n<10n<10 dimmed). The corpus-level plateau below confidence 80 (F2) is visible in individual games as well. Corpus-level calibration bins for F2: accuracy 46.4% at confidence 20-39 (n=360n=360), 43.0% at 40-59 (n=2,320n=2,320), 46.0% at 60-79 (n=3,338n=3,338), 54.6% at 80-99 (n=994n=994); the 0-19 bin (n=4n=4) is too small to interpret. Both language arms are miscalibrated in the same direction: ECE 0.150 (CI [0.109, 0.184]) in the 21 English games, 0.189 (CI [0.119, 0.256]) in the 11 Russian ones. A natural objection to F3: the legacy social-map template itself contained a suggestive sentence, “if you suspect someone, they may sense it”, so the probe could have implanted the very belief it measures. To test this we recorded two matched Russian batches of 5 games differing only in that sentence: the over-prediction appears under both wordings, 1.58 (CI [1.46, 1.77]) with the sentence and 1.48 (CI [1.39, 1.61]) without it, and in the English batch as well (1.45, CI [1.18, 1.72]). So the effect is not an artefact of the wording; whether the sentence added a little on top cannot be judged from 5 games per wording, where the intervals overlap. The case-study corpus uses the clean wording throughout. The F3 measure is a consistency between two self-reports, not ToM against engine ground truth. Under the canonical rule, agreement exceeds a trivial majority baseline on this corpus: 54.7% versus 44.7% (+10.1+10.1 p, CI [5.7,12.4][5.7,12.4]); on the legacy pre-revision corpus it fell below the same baseline (−4.3-4.3 p). The ratio itself is stable across the grid: 1.32 [1.21, 1.41] at threshold 30, 1.53 at 50, 3.05 [2.59, 3.82] at 70, smallest at the most liberal threshold, so no compressed-denominator artefact. The split survives an input control: on pairs with a confident own assessment (70+, matching Mafia’s knowing input) innocents over-predict at 1.95 [1.73, 2.21] vs Mafia 1.24 [1.08, 1.48], the confident Mafia ratio edging above 1. That gap remains threshold-dependent: Table 3 reports the F3 comparison (32 games, repaired answers, Mafia-Mafia pairs excluded) over the confidence threshold for mapping a role guess to an attitude. The majority class is “trusts” at threshold 30 and “neutral” at 50 and 70. Thr. Acc. (%) Maj. (%) Δ (p) 30 48.2 47.5 +0.7+0.7 50 54.7 44.7 +10.1+10.1 70 70.3 78.1 −7.8-7.8 Table 3: F3 sensitivity grid: agreement vs. majority baseline under different confidence thresholds (n=15,638n=15,638 pairs throughout; the staleness window is inert on this corpus and omitted). Agreement beats the baseline at the canonical threshold 50, matches it at 30 and falls below it at 70: the comparison remains threshold-dependent. Appendix D Formative User Study Protocol Within-subject, N=5N=5 researchers external to the project: two error-localization tasks on unfamiliar recorded games, one with the viewer and one on raw JSONL logs with scripting allowed; tool order and task-tool assignment are counterbalanced, 12 minutes per task. Measures: time to localization, correctness against gold labels, and SUS with three open questions on viewer affordances. The full session script, task games with gold answers, and questionnaires ship in docs/user_study_protocol.md. Appendix E Replay Experiment Details Figure 5: The viewer’s branch dialog: any timeline step of a snapshotted game can be forked into N replays server-side; branches arrive in the game list as ordinary games and render as a branch tree. Per-utterance arm contrasts for the experiment of §6 (the running-example game 36594b66 from the case-study corpus; n=5n=5 rerolls per arm; suspicion-only probes; the POST arm’s within-arm variance serves as the resampling noise floor; vote-time suspicion is the mean normalized rank of the player over suspicion_ranking probes in the fork round’s vote phase, 1 = most suspicious). In the factual game Gray was eliminated with vote-time suspicion 0.96 and Mafia won in round 2. None of the contrasts in Table 4 is statistically significant at these sample sizes: the largest, R1.8’s 2/5 vs. 4/5 eliminations, has Fisher exact (two-sided) p≈0.52p≈ 0.52, no interval on the arm difference excludes zero, and 3 utterances × 4 outcome measures are inspected without multiplicity correction. Comparisons of pivotality between utterances additionally carry a fork-position confound: a later fork leaves less stochasticity before the vote, so PRE probabilities of utterances at different timeline positions are not aligned. The table therefore documents an end-to-end run of the attribution workflow; it suggests, but does not establish, R1.8’s pivotality. R1.7 R1.8 R1.9 Speaker Logan (M) Finley (M) Casey P(Gray elim.) PRE 0.6 0.4 0.8 P(Gray elim.) POST 0.8 0.8 0.8 Δ susp. (Gray) +0.10+0.10 +0.18+0.18 −0.00-0.00 Δ susp. (Logan) −0.04-0.04 −0.11-0.11 +0.09+0.09 P(Mafia win) PRE/POST .8/.8 .8/.8 1/1 Table 4: Reroll-variance attribution over three candidate utterances of the round-1 discussion: Logan’s deflection onto Gray (R1.7), Finley’s pile-on (R1.8), Casey’s endorsement (R1.9). Δ = POST −- PRE. All 30 forks completed and ship as replayable logged games.

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