This paper presents a novel agentic LLM pipeline for SemEval-2026 Task 10 that jointly extracts psycholinguistic conspiracy markers and detects conspiracy endorsement. Unlike traditional classifiers that conflate semantic reasoning with structural localization, our decoupled design isolates these challenges. For marker extraction, we propose Dynamic Discriminative Chain-of-Thought (DD-CoT) with deterministic anchoring to resolve semantic ambiguity and character-level brittleness. For conspiracy detection, an "Anti-Echo Chamber" architecture, consisting of an adversarial Parallel Council adjudicated by a Calibrated Judge, overcomes the "Reporter Trap," where models falsely penalize objective reporting. Achieving 0.24 Macro F1 (+100% over baseline) on S1 and 0.79 Macro F1 (+49%) on S2, with the S1 system ranking 3rd on the development leaderboard, our approach establishes a versatile paradigm for interpretable, psycholinguistically-grounded NLP.
Figures & tables
Figure 1: System architecture. S1 : DD-CoT Self-Refine extracts markers; a deterministic verifier anchors them to character offsets. S2 : Anti-Echo Chamber with contrastive retrieval, forensic profiling, a Parallel Council, and a calibrated Judge.
Task
Split
Baseline F1
Agentic
Δ
S1
Dev ( 100 )
0.12
0.24
+100%
Test ( 938 )
–
0.21
–
S2
Dev ( 100 )
0.53
0.79
+49%
Test ( 938 )
–
0.75
–
Table 1: Main results (macro F1). Baseline: zero-shot GPT-5.2. Document counts in parentheses.
Component / Change
Metric
Baseline
Agentic
Δ
Subtask 1:
Self-Refine (Audit loop)
Macro F1
0.173
0.240
+0.067
DD-CoT (Perpetrator vs. Victim Discrimination)
Actor F1
0.263
0.290
+0.027
Marker Extraction
Contextual Retrieval (Dynamic few-shot examples)
Macro F1
0.243
0.240
− 0.003
Subtask 2:
Parallel Council (Debate)
Recall
0.481
0.560
+0.079
Prosecutor (Evidence for Conspiracy)
F1 Score
0.680
0.795
+0.115
Conspiracy
Calibrated Judge (Final Decision Logic)
Macro F1
0.638
0.681
+0.043
Table 2: Ablation Summary (dev) to isolate the impact of core architectural nodes. DD-CoT significantly improves Actor identification by disambiguating agency (detecting perpetrators vs. victims). Parallel Council and Contrastive Retrieval combine to suppress the “Reporter Trap,” where topical discussion of conspiracy is misclassified as endorsement.
Configuration
Latency
Tokens
Calls
S1
Baseline (Generator only)
10.5s
2,549
1.0
Full Graph (Self-Refine)
30.2s
7,986
2.5
S2
Baseline (Single Agent)
4.6s
1,665
1.0
Full Graph (Council + Judge)
29.1s
12,627
5.0
Table 3: Per-document average latency and token usage profiled on 20 dev documents. All calls are LLM API invocations; baseline is a single zero-shot prompt.
Table 4: S1 Agent Ablation (Dev Set). Breakdown of contributions from the Critic and Refiner agents.
Figure 4: Juror Ablation Study (LOO). Removing any single persona significantly degrades performance, validating the necessity of the full four-juror council.
Metric
Standard CoT (Base)
DD-CoT (New)
Δ
Actor F1
26.3%
29.0%
+2.7 pts
Victim F1
23.3%
22.0%
− 1.3 pts
Appendix
Table 5: Impact of DD-CoT on Agency Disentanglement (dev). DD-CoT significantly improves Actor detection by resolving subject-position bias, with a minor redistribution of Victim scores.
Method
F1
Acc.
Deadlock
FP Conf.
Majority Vote
0.638
0.779
66.7%
0.890
Calibrated Judge
0.681
0.805
100.0%
0.865
Appendix
Table 6: Ablation: Judge vs. Majority Vote (original).
Configuration
Macro F1
Precision
Recall
No Retrieval
0.329
0.322
0.419
Standard Retrieval (Naive)
0.296
0.292
0.376
Stratified Retrieval (Ours)
0.311
0.313
0.392
Appendix
Table 7: Few-shot retrieval strategy comparison on dev set (S1) (original).
Configuration
False Positive Rate
Reduction
Standard Retrieval
0.160
–
Contrastive Retrieval
0.080
50%
Appendix
Table 8: S2 Retrieval Ablation: Impact of Contrastive Retrieval on False Positive Rate (original).
Figure 5: Number of marker types in the dataset.
Figure 6: Label distribution for conspiracy detection.
Figure 7: Frequency per marker type.
Figure 8: Number of marker annotations per document.
Figure 9: Marker type co-occurrences
Figure 10: Mean IoU of overlapping spans across marker type pairs. Higher values indicate greater boundary ambiguity between categories.
Figure 11: Marker type distribution across Subreddits.
Figure 12: Annotation contribution.
Figure 13: Span length distribution.
Figure 14: Marker span mass.
Figure 15: Normalized position of marker spans within documents (0 = start, 1 = end). KDE per marker type.
Parameter
Value
Description
Population size
20–30
Number of prompt candidates
Max generations
40–80
Budget (max_metric_calls)
Tournament size
3
Selection pressure
Mutation rate
0.2
Probability of mutating survivors
Crossover model
GPT-5.2
LLM for semantic merging
Reflector model
GPT-5.2
LLM for mutation feedback
Appendix
Table 9: GEPA hyperparameter configuration for S1 and S2 optimization.
Text Snippet
Baseline
Our System (Agentic)
“The public was manipulated by the media…”
Actor : The public
Actor : the media Victim : The public
“The article claims that the earth is flat.”
Endorsement
Neutral Reporting
Appendix
Table 10: Qualitative example demonstrating the resolution of agency and mitigation of the Reporter Trap by the agentic pipeline.