Research Benchmark Statistical Inference Engine Zero Emojis Enforced

Adaptive Agent Memory Resilience Playground

Interactive evaluation of Bayesian Beta-Bernoulli trust updating, epistemic variance quantification, pessimistic Lower Confidence Bound (LCB) composite retrieval, and Theorem 1 statistical quarantine for autonomous LLM agents.

Author: Sumit Das (@sumitaidev) Benchmark: Hugging Face Dataset Kaggle: Kaggle Benchmarks Paper: arXiv:2026.xxxxx

Prior Parameters

Prior Alpha (alpha_0) 3.0
Prior Beta (beta_0) 1.0

Observed Experience Trials

Task Successes (n_s) 0
Task Failures (n_f) 0

Inference & Risk Hyperparameters

Risk Aversion (lambda) 1.0
Admissibility Threshold (theta_0) 0.70
Significance Level (gamma) 0.05
Posterior Mean E[theta]
0.7500
Uncertainty (sigma)
0.1936
LCB Score (lambda=1.0)
0.5564
P(theta > threshold)
65.70%
OPERATIONAL STATUS: ACTIVE (Admissible for Agent Memory Bank) P(theta > 0.70) = 65.70% >= 5.00%

Posterior Distribution & Uncertainty Quantification

Theorem 1 Verification: Consecutive Failure Trajectory

Minimum consecutive failures to trigger deterministic quarantine: t* = min { t in N : P(theta > 0.70 | 0, t) < 0.05 } = exactly 4 trials under weakly-informative prior Beta(3, 1).

Consecutive Failures (t) Posterior Distribution Posterior Mean E[theta] Epistemic Uncertainty (sigma) P(theta > threshold) Quarantine Status

Pessimistic LCB Memory Retrieval vs Naive Vector Search

Simulates a real-world task query where an agent must select an episodic strategy. Corrupted strategies repeatedly retrieve high semantic cosine similarity, causing cascading failures under naive RAG. Our composite score: Score(e; q) = Sim(q, v_e) * LCB_lambda(e) suppresses corrupted memories and penalizes untested uncertainty.

Retrieval Composite Scoring: Score(e; q) = Sim(q, v_e) * max(0.0, E[theta | n_s, n_f] - lambda * sqrt(Var[theta | n_s, n_f]))
Risk Aversion Parameter (lambda) 1.0
LCB Rank Memory ID Domain Semantic Cosine Sim Successes (n_s) Failures (n_f) Posterior Mean LCB Score Naive Score (Sim) Proposed Composite Score Status Operational Directive
Analytical Inspection:
  • lambda = 0.0 (Naive Cosine): MEM-CORRUPT-01 ranks #1 (score 0.940) despite 5 empirical failures. Agent succumbs to negative transfer.
  • lambda >= 1.0 (Pessimistic LCB): MEM-CORRUPT-01 is pruned or suppressed. MEM-ROBUST-02 (22 successes, 1 failure) correctly assumes rank #1.
  • MEM-UNTESTED-03: Has high semantic similarity (0.890) but zero execution history; its epistemic uncertainty penalty prevents naive over-reliance.

Empirical Benchmark Telemetry (1,200 Execution Steps)

Experimental comparison across 4 controlled conditions under an adversarial noise injection window (steps 60 to 140).

Ablation Condition Total Steps Task Accuracy Average Reward Mean Cosine Sim Quarantine Triggers Core Mechanism
Condition A: Vanilla Baseline 300 48.33% 0.483 0.000 0 Zero inter-task memory. Agent approaches each task tabula rasa.
Condition B: Naive Vector RAG 300 32.67% 0.327 0.824 0 Semantic cosine similarity retrieval. Collapses under memory poisoning.
Condition C: Symmetric Reflexion 300 61.25% 0.613 0.789 0 Symmetric Exponential Moving Average (EMA). Lacks uncertainty bounds.
Condition D: Adaptive Bayesian LCB (Proposed) 300 84.58% 0.846 0.742 42 Conjugate Beta-Bernoulli, LCB composite retrieval, Theorem 1 quarantine. (+51.91% over naive RAG).

Academic Citation & Reproducibility

Use the following BibTeX entry to cite this benchmark and methodology in research:

@article{das2026adaptive, title={Adaptive Agent Memory Resilience: Mitigating Negative Transfer and Memory Poisoning via Bayesian Trust Updating and Pessimistic Lower Confidence Bound Retrieval}, author={Das, Sumit}, journal={arXiv preprint}, year={2026} }