| name | dover-auto-debugging |
| title | DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2512.06749 |
| keywords | ["multi-agent systems","auto-debugging","intervention-driven","failure recovery","agent coordination"] |
| description | Diagnose and fix multi-agent system failures through targeted interventions (message edits, plan changes) rather than static log analysis. DoVer recovers 18-28% of failed trials with 30-60% hypothesis validation—essential for autonomous multi-agent reliability. |
Overview
DoVer augments traditional log-based debugging with active verification through targeted system interventions. Rather than accepting single-point attributions, the framework systematically tests modifications to agent communications and planning to determine which changes resolve failures, providing practical mechanisms for improving multi-agent reliability.
When to Use
- Multi-agent systems experiencing task failures
- Debugging attribution is uncertain or insufficient
- Need to determine which modifications resolve failures
- Testing multiple potential fixes for the same failure
- Applications where failure recovery is critical
- Autonomous agent systems requiring reliability improvement
When NOT to Use
- Single-agent systems or simple pipelines
- Deterministic workflows without failure modes
- Cases where static log analysis suffices
- Real-time systems where intervention overhead is unacceptable
- Scenarios where testing modifications on live systems is risky
Core Technique
Hypothesis generation and verification through targeted interventions:
class DoVerDebugger:
def __init__(self, agent_framework):
self.framework = agent_framework
self.failed_trials = []
def analyze_failure(self, trial):
"""
Analyze failed trial to generate debugging hypotheses.
Goes beyond log analysis to test interventions.
"""
hypothesis_candidates = []
history = trial.execution_trace
agents = trial.agents
final_state = trial.final_state
task = trial.task
for agent in agents:
hypothesis = {
'type': 'agent_decision',
: agent,
:
}
hypothesis_candidates.append(hypothesis)
agent_pair .get_agent_pairs(agents):
hypothesis = {
: ,
: agent_pair,
:
}
hypothesis_candidates.append(hypothesis)
hypothesis = {
: ,
:
}
hypothesis_candidates.append(hypothesis)
hypothesis_candidates
():
hypothesis[] == :
.test_agent_intervention(trial, hypothesis)
hypothesis[] == :
.test_message_intervention(trial, hypothesis)
hypothesis[] == :
.test_plan_intervention(trial, hypothesis)
():
agent = hypothesis[]
original_trial = trial
alternatives = .generate_alternative_actions(
agent,
original_trial.execution_trace
)
results = []
alternative_action alternatives:
modified_trial = .run_with_intervention(
original_trial,
agent,
alternative_action
)
success = modified_trial.completed_successfully
results.append({
: alternative_action,
: success ,
: modified_trial
})
results
():
agent_pair = hypothesis[]
agent_a, agent_b = agent_pair
messages = .extract_messages(
trial.execution_trace,
agent_a,
agent_b
)
results = []
message messages:
improved_messages = .improve_message(message)
improved_msg improved_messages:
modified_trial = .run_with_message_edit(
trial,
message,
improved_msg
)
success = modified_trial.completed_successfully
results.append({
: message,
: improved_msg,
: success
})
results
():
original_plan = trial.plan
problem_stage = .identify_problem_stage(trial)
alternative_plans = .generate_alternative_plans(
trial.task,
original_plan,
problem_stage
)
results = []
alt_plan alternative_plans:
modified_trial = .run_with_plan_intervention(
trial,
alt_plan
)
success = modified_trial.completed_successfully
progress = .measure_progress(modified_trial)
results.append({
: alt_plan,
: success ,
: progress
})
results
():
successful_interventions = [
r r intervention_results
r[] ==
]
(successful_interventions) > :
, successful_interventions
:
progress_results = [
r r intervention_results
r.get(, ) >
]
(progress_results) > :
, progress_results
:
, []
():
.framework.apply_intervention(
trial_id,
successful_intervention
)
recovered_trial = .framework.retry_with_intervention(
trial_id,
successful_intervention
)
recovered_trial
