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You cannot improve what you cannot trace.We create agents to achieve some desired goal. As the agent progresses, we introduce new constraints. But how can we be sure these constraints actually work?
Conversational shopping agent that finds the right product in 10 questions, works offline, made to work in real world situations.
An offline shopping copilot that turns evolving conversations into precise product recommendations.
An autonomous research agent that reads the problem, writes the pipeline, and iterates — no human in the loop. Proposes research ideas, tests them, and logs everything by itself.
State-bound release control for autonomous agents: certify candidates by diffing real effects, then gate promotion with an audited, tamper-evident ledger.
Learning-guided search synthesizes, benchmarks, and deploys shape-specialized Transformer programs on RTX 4080, achieving 14.49x geometric-mean speedup over the PyTorch baseline on Shapes 1-13.
An LLM agent that autonomously proposes, codes, tests, and iterates on recsys models for KuaiRand-Pure — zero manual intervention, +0.0022 delta over baseline, full run-by-run transparency.
One stop AI image detector
AI-generated image detection that stays accurate even after compression, blur, and cropping
(No LLM Needed!) Shopping Copilot: Context-Aware Conversational Product Search- Edsel Tan
Robust AIGI Detection using Modulated LoRC.
The hardest AI image to catch is the one the internet has already changed. GLowCLIP fuses global context and local forensic traces to detect it after compression, blur, crop, and noise.
A constraint-aware conversational search agent for TechJam 2026 Problem Statement 4. The agent turns vague or changing shopping requests into ranked products from a frozen 50,000-item Amazon catalog.
Multiple models. One verdict. Stronger image verification
claude driven image detection model development. blatantly token-maxxed.
n offline conversational shopping agent that tracks changing intent, asks useful clarifying questions, and quickly ranks the right products from a 50,000-item catalog.
Security - it's the big question that our current agentic AI solutions need to answer. With this middleware, account-based authentication, isolation, role-control are strictly controlled.
A deterministic 2-level Bayesian shopping agent achieving 0.9744 TechnicalScore across 50k products with zero LLM calls, sub-15ms latency, and expected utility depth.
An LLM agent that runs the full ML research loop on a frozen benchmark: proposes an experiment, edits real model code, trains, and keeps it if a bootstrap test says the gain beats measurement noise.
claude driven gpu kernel optimization. blatantly tokenmaxxed.
Detect AI-generated images and test confidence under real-world transformations such as compression, blur, resizing, noise, colour changes and cropping, all through an interactive Gradio demo.
AI-image detection that survives the real internet. Compression takes off-the-shelf detectors from 71% to under 1% — we hold 83% worst-case, and every verdict carries a measured confidence grade.
Clanker-in-the-middle: Middleware that watches, gates, and logs every move your AI coding agent makes.
Robust AI-generated image detection using RGB and frequency-domain analysis.
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