PolyGnosis 2.0: Multi-Agent Architecture for Prediction Market Intelligence via Harness Engineering
PolyGnosis 2.0 introduces a multi-agent system that synthesizes Polymarket prediction market signals with GDELT OSINT streams to identify 'Perspective Mismatches' as trading signals. The paper rigorously evaluates agentic harness engineering techniques—reflection loops, tool-calling, divide-and-conquer partitioning, and chain-of-thought—in high-noise financial domains. Key empirical findings include that structural partitioning is necessary for multi-dimensional alignment, but unconstrained terminal reflection induces logical drift, and a pervasive consensus bias emerges across agent configurations. The authors identify a Pareto-optimal configuration achieving professional-grade analytical precision with minimized latency and token overhead.
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CHRONOS: Temporally-Aware Multi-Agent Coordination for Evolving Data Marketplaces
CHRONOS is a three-layer multi-agent architecture addressing temporal degradation in knowledge-graph data marketplaces, combining neural-ODE-based shortcut decay, changepoint-conditioned Shapley pricing, and EXP3-IX-driven differential privacy budget management. The system achieves 0.937 recall@10, 2.74 QPS, and 161ms latency under a total epsilon of 4.25 (delta=1e-6) using zCDP composition across four benchmarks. A key limitation noted is that at this privacy level, released valuations remain noise-dominated, with utility primarily derived from public index routing. The work provides formal guarantees including per-query recall-loss bounds and finite-sample Shapley error bounds under distribution shift.
MA²P: A Meta-Cognitive Multi-Agent Framework for Complex Persuasion
The paper introduces MA²P, a multi-agent framework designed for complex persuasion tasks where the persuadee's internal states are latent. The system coordinates perception management, mental-state inference, strategy execution, memory, and evaluation modules, and adds a meta-cognitive configurator that selects domain-appropriate strategies from a structured knowledge base to reduce cross-domain performance variance. Experiments show higher persuasion success rates compared to baselines. The work addresses a known weakness of LLMs in producing generic or weakly grounded persuasive responses.
Agentopia: Long-term multi-agent life simulation framework for training LLMs on social behavior
Researchers introduce Agentopia, a framework for simulating 10 years of social life across 100 LLM-powered agents, enabling study of emergent social behaviors and long-term personal growth dynamics. The system defines a 'life reward' metric mirroring human well-being and uses it to train LLMs via rejection sampling. Training on simulated social experience yields a +15.6% improvement on downstream role-playing benchmarks, suggesting that synthetic social simulation can generalize to real capability gains.
TradingAgents: Multi-Agent LLM Financial Trading Framework
TradingAgents is an open-source Python framework by TauricResearch that applies multi-agent LLM architectures to financial trading tasks. The repository has accumulated 81,650 GitHub stars with 284 added today, indicating strong community traction. It represents a concrete deployment pattern for agentic AI systems in quantitative finance.
Code as Agent Harness: A Survey of Code as Operational Substrate for Agentic AI Systems
This survey paper introduces the concept of 'code as agent harness,' framing code not merely as output but as the operational infrastructure for LLM-based agents—covering reasoning, action, environment modeling, and execution-based verification. The authors organize the analysis across three layers: harness interface, harness mechanisms (planning, memory, tool use, feedback control), and scaling to multi-agent systems. Applications span coding assistants, GUI/OS automation, embodied agents, scientific discovery, and enterprise workflows. Open challenges include evaluation beyond task success, verification under incomplete feedback, and human oversight for safety-critical actions.
DexHoldem: A Real-World Benchmark for Dexterous Embodied Agents Using Texas Hold'em Manipulation
DexHoldem is a new system-level benchmark for evaluating dexterous embodied agents on a ShadowHand robot performing Texas Hold'em card manipulation tasks. It provides 1,470 teleoperated demonstrations across 14 manipulation primitives, a physical policy benchmark, and an agentic perception benchmark for structured game-state recovery. Top performers include π₀.₅ at 61.2% task completion and Claude Opus 4.7 at 34.3% strict perception accuracy, with GPT 5.5 achieving 66.8% field-wise accuracy. The benchmark exposes gaps between isolated visual sub-capabilities and full closed-loop embodied decision-making.
AgentSpec: A modular framework for controlled composition and analysis of embodied LLM agent scaffolds
AgentSpec is a new modular specification framework that represents embodied LLM agents as typed compositions of reusable policy components with standardized interfaces across perception, memory, reasoning, reflection, action, and learning modules. The framework enables controlled swapping and recombination of components, instantiated across four benchmarks (DeliveryBench, ALFRED, MiniGrid, RoboTHOR). Key findings include that agent performance is governed by scaffold compatibility and interaction effects rather than isolated module strength, and that RL-trained policies compose best when optimized with deployment-time scaffold structure. Code, baselines, and an interactive playground are publicly released.
Stance Detection in Prediction Market Commentary via Counterfactual Augmentation and Market Context
This paper introduces the first stance detection system applied to prediction market commentary (Polymarket), addressing extreme class imbalance (8.7% anti-market comments) through LLM-driven counterfactual augmentation using the Anthropic API. RoBERTa-base is fine-tuned across a 4×3 ablation covering input configurations and augmentation doses. Key findings: market context is the dominant factor (raising 3-class Anti recall from 0.10 to 0.45), 50% synthetic augmentation is optimal, and full augmentation (100%) consistently degrades performance. Attention-based interpretability supports all three findings mechanistically.