Explore/agent app/CHAL: Council of Hierarchical Agentic Language
C

Tommaso Giovannelli, Griffin D. Kent/CHAL: Council of Hierarchical Agentic LanguageUnknown

Multi-agent debate has emerged as a promising approach for improving LLM reasoning on ground-truth tasks, yet current methodologies face certain structural limitations: debate tends to induce a martingale over belief trajectories, majority voting accounts for most observed gains, and LLMs exhibit confidence escalation rather than calibration across rounds. We argue that the genuine value of debate, and dialectic systems as a whole, lies not in ground-truth tasks but in defeasible domains, where every position can in principle be defeated by better reasoning. We present the Council of Hierarchical Agentic Language (CHAL), a multi-agent dialectic framework that treats defeasible argumentation as an engine for belief optimization. Each agent maintains a CHAL Belief Schema (CBS), a graph-structured belief representation with a Bayesian-inspired architecture, that facilitates belief revision through a gradient-informed dynamic mechanism by leveraging the strength of the belief's thesis as a differentiable objective. Meta-cognitive value systems spanning epistemology, logic, and ethics are elevated to configurable hyperparameters governing agent reasoning and adjudication outcomes. We provide a series of ablation experiments that demonstrate systematic and interpretable effects: the adjudicator's value system determines the debate's overall trajectories in latent belief space, council diversity refines beliefs for all participants, and the framework generalizes across broad fields. CHAL is, to our knowledge, the first framework to treat multi-agent debate as structured belief optimization over defeasible domains. Further, the auditable belief artifacts it produces establish the foundation for dedicated evaluation suites for defeasible argumentation, with broader implications for building AI systems whose reasoning and value commitments are transparent, aligned, and subject to human oversight.

agent app
GitHubCompare
Refreshed 4d ago
OverviewActivity52wAlternativesDocs
Stars0
Forks0
HF Downloads30d
Last commit
Refreshed4d ago
Project healthUnknownNo activity data.
Production readinessResearch / EarlyBest for exploration and prototyping.
Risk notesUnknown licenseVerify license before production use.
AgentHub Score
48 / 100
Composite score from 6 signals. How we score →
Active project
48Score
Growth
40C
Activity
30C
Documentation
70C+
Maturity
45C
Community
42C
Production
58C
GitHub stars · 90 days0 +0.0%
30d90d1y
latest release
Commit activity · 52 weeksActive contributor activity
LowHigh
JunSepDecMarNow
Practical assessment
Should you use it?

✓ Best for

  • Research and experimentation
  • Prototype development
  • Learning agentic patterns

◎ Strengths

  • Active community
  • Open source
  • Well-documented API

✕ Not ideal for

  • Untested at scale without validation
  • Teams without AI/ML expertise

⚠ Watch-outs

  • Review changelog before updating
  • Verify license for commercial use
Technical details
What's inside
Language
License
Sourcearxiv
Open source✗ No
Commercial use
Docs
Demo

AgentHub Score

48
Score 48/100
Below average

Alternatives

C
crewai
26.1k · Multi-Agent
87
A
autogen
42.7k · Multi-Agent
71
S
smolagents
11.2k · Coding
84
O
openai-agents-python
9.4k · Multi-Agent
81
Compare all →

Recent activity

Latest commit —
Indexed by AgentHub crawler4d ago
Monitor for new releasesongoing