research

Papers and ongoing investigations

Peer-reviewed work, work-in-progress papers, and research-grade builds.

Recent

CNSM 2026 Submitted

Match Your Loss to Your Cost: Asymmetric Losses and Conformal Capacity Bands for Backbone Traffic Forecasting

A network operator pays far more for a capacity shortfall than for spare headroom, so this trains the traffic forecaster on that real cost instead of on RMSE.

Mean overload rate against mean over-provisioning cost on Abilene as the training ratio is swept from 1:1 to 100:1. Overload falls by more than two orders of magnitude as cost rises, and the 1:1 point coincides with the MSE baseline.Mean overload rate against mean over-provisioning cost on Abilene as the training ratio is swept from 1:1 to 100:1. Overload falls by more than two orders of magnitude as cost rises, and the 1:1 point coincides with the MSE baseline.
Sweeping the training ratio on Abilene (DLinear, 20 seeds). The 1:1 point lands on the MSE baseline exactly, since α = β = 1 recovers MSE.
  • Cusp-linear loss matched to operator ratio: +76% Abilene, +75% GÉANT, +54% CESNET vs MSE at top operator asymmetry. L1 is the canonical consistent scoring rule for the τ-quantile (Gneiting 2011); squared asymmetric collapses on heavy-tailed GÉANT.
  • Cross-architecture: the matched 5:1 win reproduces on DLinear (+30 to +97%) and iTransformer (+28 to +79%) across Abilene/GÉANT/CESNET.
  • ACI vs split CQR: overload rate 155× lower on Abilene, 9.1× lower on GÉANT, 3.8× lower on CESNET. ACI's across-seed coverage variance is 30 to 200× smaller.
  • Python
  • PyTorch
  • statsmodels
  • NumPy
  • Pandas
  • scikit-learn
Read the writeupGitHub
LCTES 2026 Published

Scheduled Partial-Credit RL for Reliable Code Generation with Small Language Models (WIP)

Handing a small model from binary rewards to partial credit partway through training lifts its syntax-valid output from 18% to 63%.

  • On DeepSeek-Coder-1.3B over 100 APPS+ prompts: SFT 44% syntax / 3% ≥1-pass. Binary-reward PPO degrades to 18% / 0%. Partial-credit from scratch reaches 27% / 2%.
  • The binary-to-partial-credit schedule (PPO-continue) wins: 63% syntax, 9% ≥1-pass, 2% all-pass on a single attempt. Curriculum on the reward matters more than the reward shape alone.
  • LoRA r=16 (6.3M trainable params, 0.47%), single V100 16GB, Bandit-graded R_sec. Security null on APPS+ (algorithmic); CWE-mapped partial credit is the next step.
  • Python
  • PyTorch
  • TRL (PPO)
  • PEFT (LoRA)
  • Bandit
  • DeepSeek-Coder-1.3B
  • APPS+
Read the writeupPaper (ACM DL)GitHub
IEEE AITest 2025 Published

Fixing Performance Bugs Through LLM Explanations

Training a model on written explanations of Java performance bugs, rather than on labels alone, raises detection accuracy from 67% to 84%.

  • Curated dataset of 490 performance bugs across 17 Defects4J projects, with a 5-category taxonomy (algorithmic, memory, CPU, redundant, I/O).
  • Fine-tuned GPT-4o-mini to produce explanations alongside predictions. Detection accuracy 67.3% → 83.7%, F1 64.6% → 82.3%.
  • Full reproduction stack public: extraction, categorization, fine-tuning, evaluation harness.
  • Python
  • GPT-4o-mini fine-tuning
  • Defects4J
  • Java
  • OpenAI API
Read the writeupPaper (IEEE)Project siteGitHub