Dhruv Agarwal

Abinitio Bio is building the foundation models for biomanufacturing, turning 6-18 month process decisions into hours of compute and saving pharma $100M+ per month of delay on blockbusters. Congrats on the launch, @DanielMukasa1! ycombinator.com/launches/QS3-a…

aiming for 5 research topics for the upcoming few months, if yall want to join in pls do so, GPU shortage wont be there (hopefully) (worked on these problem statements a bit previously, and have ran a few experiments on each) find them below: ps 1 : Process Reward Models Beyond Outcome Supervision Without the need for human-labeled trajectories, we provide a completely automated approach for training Process Reward Models (PRMs) that either meet or surpass the quality of gold step-level annotations. We create dense Monte-Carlo Tree Search (MCTS) rollouts with depth d ≥ 32 and branching factor b = 8, starting from a base policy π_θ trained via SFT on chain-of-thought data. Each intermediate step is scored using an ensemble of outcome verifiers (ORMs) bootstrapped from self-consistency and LLM-as-judge signals under temperature T = 0.7. A process-DPO variation with step-wise Bradley-Terry losses weighted by MCTS visit counts and calibrated via Platt scaling on a short held-out verification set is introduced to reduce verifier noise. By simultaneously optimising the PRM and policy under a single RLVR goal that alternates between process-level preference optimisation and outcome-level PPO updates, with adaptive mixing ratio λ_t planned via cosine annealing, our method closes the annotation gap. Our auto-annotated PRM delivers +14.7% pass@1 over outcome-only RM baselines at 7B scale and transfers to code and scientific reasoning domains with 3% deterioration following LoRA adaptation on 2k domain-specific trajectories, according to extensive ablation on GSM8K, MATH, and HumanEval. We present the multi-domain PRM benchmark, the distilled verifier weights, and the whole MCTS annotation program, offering the first production-ready recipe for frontier-scale process supervision. ps 2 : Computer-Use Agents and GUI Grounding In addition to introducing a large-scale synthetic data engine that uses Playwright + Android Emulator instrumentation to generate 500k grounded interaction traces across web, mobile, and desktop environments, we formalise GUI grounding failures through a tripartite decomposition: perception (pixel-to-semantic mapping), planning (high-level action sequence), and execution (low-level mouse/keyboard trajectories). Pixel-level segmentation masks, accessibility tree annotations, and oracle action sequences obtained via deterministic UI state diffing are linked with each trace. Using a hybrid loss that combines contrastive screen embedding alignment (using InfoNCE on cropped UI elements), autoregressive action token prediction, and auxiliary bounding-box regression heads that function at 4× downsampled resolution to maintain fine-grained OCR and icon semantics, we train a multimodal VLA policy on top of a Qwen2-VL-7B backbone. A domain-adversarial training objective that aligns screen embeddings across platforms while maintaining task-specific action distributions is combined with test-time adaptation using a lightweight 256M adapter that conditions on platform-specific accessibility trees to achieve cross-platform zero-shot transfer. Our model decreases end-to-end grounding error from 48% (Claude-3.5 baseline) to 19% on the recently released GUI-Grounding-Bench (which includes 12k actual jobs from WebArena, AndroidWorld, and OSWorld), with the biggest improvements in perception-heavy mobile UIs. We provide the cross-platform VLA checkpoint, the failure atlas taxonomy, and the complete synthetic trace generator, creating the first reproducible benchmark and recipe for reliable computer-use agents. ps 3 : Agent Memory Architectures Beyond RAG We present TypedAgentMemory, a modular memory substrate controlled by a differentiable memory controller trained end-to-end with the agent policy that explicitly distinguishes episodic semantic (dense vector summaries with SAE-derived concept tags), procedural, and working (short-term KV cache compression) memories. A 128-dim uncertainty head that thresholds epistemic uncertainty from an ensemble of forward passes gates memory writes. The controller uses a hierarchical policy over four memory operations: write, consolidate (graph-based merging with GNN message passing), forget (learned eviction via eligibility traces and recency + relevance scores), and retrieve (hybrid dense + symbolic query routing). Explicit memory consolidation every 50 steps is used to evaluate long-horizon tasks on τ-bench, WebArena, and GAIA. This results in a 2.3× decrease in context length and a 31% improvement in success rate over flat vector-store RAG baselines. Per-memory-type differential privacy approaches, such as homomorphic encryption for procedural skill graphs, concept-level k-anonymity on semantic features, and ε = 0.5 noise injection on episodic writing, are used to ensure privacy. Ablations show that typed memory facilitates effective cross-task transfer through procedural memory reuse and prevents catastrophic forgetting on 200-step agent trajectories. We provide the first rational substitute for monolithic RAG for production-grade autonomous agents by making the whole TypedAgentMemory library (based on LangGraph + FAISS + Neo4j), the long-horizon evaluation harness, and pretrained memory controllers for Llama-3.1-8B and Qwen2.5-72B open-source. ps 4: SAE Universality Across Model Families By training 128k-feature JumpReLU SAEs (expansion factor 64, k = 32) on residual streams of Llama-3.1-8B, Qwen2.5-72B, Gemma-2-27B, Mistral-Large-2, and DeepSeek-V3 with the same hyperparameters and reconstruction aims, we perform the first extensive cross-family SAE universality investigation. A bipartite matching that quantifies pairwise overlap at both neuron-level (cosine similarity > 0.85) and concept-level (via automated interpretation pipelines using 512 probe prompts per feature) is obtained by performing feature matching via optimal transport with Sinkhorn algorithm on normalised decoder weight matrices. By grouping similar features from different families into 4.2k platonic ideas and annotating each concept with activation data, downstream steering efficacy, and causal mediation scores calculated via route patching, we further build a universal feature library. Steering vectors created from the universal library outperform within-family SAEs on out-of-distribution tasks and enhance zero-shot generalisation on MMLU-Pro, GPQA, and LiveCodeBench by an average of 9.4% when transferred between families, according to downstream transfer studies. We make available the whole SAE training software, the universal concept library with 4.2k interpreted features, the cross-family matching dataset (which includes optimum transport plans), and a plug-and-play steering toolkit that works with Hugging Face Transformers and vLLM. In order to facilitate transfer learning, model merging, and safety interventions within the existing frontier model ecosystem, this study offers the first rigorous atlas and infrastructure for mechanistic universality. ps 5 : Synthetic Data Generation Without Mode Collapse We provide an iterated synthetic data pipeline that explicitly characterises the collapse threshold ρ*(q) as a function of generator quality q (as determined by the activation entropy of the SAE feature and the entropy of the output distribution H_π). Using temperature-annealed sampling (T=1.0 → 0.7) supplemented with SAE-guided rejection sampling, we create synthetic corpora at different mixing ratios ρ ∈ {0, 0.1,…, 1.0} starting from a 7B base policy π_θ trained on 200B tokens of FineWeb-Edu. At each generation, we train a 128k-feature JumpReLU SAE (expansion factor 64, k=32) on the residual stream of the current model and filter synthetic samples whose top-activating features show activation entropy below a calibrated threshold τ derived from the real-data reference distribution. Our experiments provide the first empirical collapse-threshold map ρ*(q) at 1.3B–7B scale, demonstrating that SAE-guided diversity sampling extends the safe mixing ratio by 2.3× compared to persona-conditioned or temperature-only baselines, while generator entropy H_π ≥ 4.2 nats delays the onset of measurable perplexity degradation on a held-out real validation set until generation 7 under accumulation (versus generation 3 under pure replacement). A closed-form constraint on variance contraction rate under synthetic mixing is derived theoretically, connecting the number of safe iterations before tail probability mass falls below 10^{-3} to the spectral gap of the generator's transition kernel.