anti-resonance 0.2.0

Counteract positive recall bias in memory-augmented AI systems. Zero dependencies.

anti-resonance

Counteract positive recall bias in memory-augmented AI systems. Zero dependencies.

Install

pip install anti-resonance

Quick Start

from anti_resonance import ensure_negative_ratio

# Your RAG/memory system returned 10 results — mostly wins
results = [
    {"text": "Trade won +$150", "pnl": 150, "score": 0.9},
    {"text": "Trade won +$80",  "pnl": 80,  "score": 0.8},
    {"text": "Trade won +$200", "pnl": 200, "score": 0.85},
    {"text": "Trade won +$50",  "pnl": 50,  "score": 0.7},
    {"text": "Trade won +$120", "pnl": 120, "score": 0.75},
]

# Losses from your full memory pool
candidates = [
    {"text": "Trade lost -$90 (SL hit in ranging market)",  "pnl": -90,  "score": 0.6},
    {"text": "Trade lost -$200 (gap against position)",     "pnl": -200, "score": 0.4},
    {"text": "Trade lost -$45 (spread widened at news)",    "pnl": -45,  "score": 0.5},
]

# Force at least 20% losses into the context window
balanced = ensure_negative_ratio(
    results, candidates,
    min_negative_ratio=0.2,
    score_key="score",         # score-aware: replaces lowest-score wins
)

The agent now sees both wins AND losses before making its next decision.

The Problem: Parametric-External Memory Resonance

When an LLM-based agent retrieves memories to inform a decision, two bias sources resonate with each other:

1. Parametric bias (inside the LLM): Language models trained on internet text have a built-in optimism bias. Success stories, best practices, and positive outcomes are overrepresented in training data.

2. External memory bias (in the retrieval system): Similarity-based retrieval (vector search, embedding lookup) clusters positive outcomes together. Winning trades look alike ("strong momentum", "breakout confirmed"). Losing trades are diverse and scattered ("gap down", "spread spike", "regime change") — they don't cluster, so they rank low in similarity search.

When the LLM's parametric optimism meets a one-sided positive context window, the biases amplify each other. The model becomes dangerously overconfident because both its training and its retrieved evidence agree.

This is Parametric-External Memory Resonance — a feedback loop between the model's internal beliefs and the retrieval system's structural bias toward positive outcomes.

The Fix

Break the resonance by forcing counterexamples into the context window. anti-resonance does this at the post-retrieval stage:

Query → Retrieval → [anti-resonance] → LLM
                          ↑
                    Force 20% negatives
                    into the result set

The algorithm is simple:

1. Count negative items in top-K results
2. If ratio >= threshold → return unchanged
3. Otherwise → replace lowest-scoring positives with highest-scoring negatives from the candidate pool
4. Re-sort by score

Score-aware replacement (v0.2.0) ensures you sacrifice the least-relevant positive items and inject the most-relevant negative ones.

API

ensure_negative_ratio(results, candidates, min_negative_ratio=0.2, negative_classifier=None, score_key=None)

Parameter	Type	Default	Description
results	list[dict]	required	Top-K retrieval results
candidates	list[dict]	required	Full candidate pool
min_negative_ratio	float	0.2	Minimum negative fraction [0.0, 1.0]
negative_classifier	Callable	None	Returns True for negative items. Default: item["pnl"] < 0
score_key	str	None	Dict key for relevance score. Enables score-aware replacement

Returns: New list (same length) with at least min_negative_ratio negatives.

ensure_negative_balance

Alias for ensure_negative_ratio (backward compatibility with TradeMemory).

Use Cases

Trading Memory

balanced = ensure_negative_ratio(
    recalled_trades, all_trades,
    min_negative_ratio=0.2,
    score_key="owm_score",
)
# Agent sees losing trades alongside winners before deciding

RAG Pipelines

balanced = ensure_negative_ratio(
    search_results, full_corpus,
    negative_classifier=lambda doc: doc["sentiment"] < 0,
    score_key="relevance",
)
# LLM sees failure cases alongside success stories

Customer Support

balanced = ensure_negative_ratio(
    similar_tickets, all_tickets,
    negative_classifier=lambda t: t["resolution"] == "unresolved",
)
# Agent considers unresolved similar cases, not just resolved ones

Related Work

• FinMem (Yu et al., 2024) — FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design. Identified the echo chamber problem in LLM trading agents where memory retrieval reinforces existing beliefs. arXiv:2311.13743

• TradeMemory Protocol — Open-source trading memory layer where anti-resonance was first implemented. The ensure_negative_balance() function in TradeMemory's hybrid recall pipeline is the production origin of this package. GitHub

• Building Anti-Resonance for AI Trading Agents (Sean, 2026) — DEV.to article explaining why RAG-based trading agents develop confirmation bias and how to fix it. DEV.to

License

MIT