arXiv subjects

Simran Chana

Publications and source records attributed to Simran Chana.

4 recordsLinked to original sources

arXiv2026

Facts as First Class Objects: Knowledge Objects for Persistent LLM Memory

Large language models increasingly serve as persistent knowledge workers, with in-context memory - facts stored in the prompt - as the default strategy. We benchmark in-context memory against Knowledge Objects (KOs), discrete hash-addressed tuples with O(1) retrieval. Within the context window, Claude Sonnet 4.5 achieves 100% exact-match accuracy from 10 to 7,000 facts (97.5% of its 200K window). However, production deployment reveals three failure modes: capacity limits (prompts overflow at 8,000 facts), compaction loss (summarization destroys 60% of facts), and goal drift (cascading compaction erodes 54% of project constraints while the model continues with full confidence). KOs achieve 100% accuracy across all conditions at 252x lower cost. On multi-hop reasoning, KOs reach 78.9% versus 31.6% for in-context. Cross-model replication across four frontier models confirms compaction loss is architectural, not model-specific. We additionally show that embedding retrieval fails on adversarial facts (20% precision at 1) and that neural memory (Titans) stores facts but fails to retrieve them on demand. We introduce density-adaptive retrieval as a switching mechanism and release the benchmark suite.

cs.AI↗

arXiv2026

Selective Memory for Artificial Intelligence: Write-Time Gating with Hierarchical Archiving

Retrieval-augmented generation stores all content indiscriminately, degrading accuracy as noise accumulates. Parametric approaches compress knowledge into weights, precluding selective updates. Neither mirrors biological memory, which gates encoding based on salience and archives rather than deletes superseded information. We introduce write-time gating that filters incoming knowledge objects using composite salience scores (source reputation, novelty, reliability) while maintaining version chains that preserve prior states. Using real LLM evaluation without oracle access to quality labels, write gating achieves 100 percent accuracy versus 13 percent for ungated stores. The critical finding emerges under distractor scaling: at 8:1 distractor ratios, read-time filtering (Self-RAG) collapses to 0 percent while write gating maintains 100 percent, revealing a structural advantage of write-time over read-time curation. Validation on Wikipedia (20 entities), procedurally generated pharmacology data, and 2026 arXiv papers confirms these findings. The gating advantage scales inversely with parametric memory support: +25pp for Wikipedia, +48pp for post-cutoff arXiv, +65pp for procedural data with zero training knowledge. Signal ablation confirms the method does not depend on oracle-correlated metadata. Write gating matches Self-RAG accuracy at one-ninth the query-time cost.

cs.AI↗

arXiv2026

ANML: Attribution-Native Machine Learning with Guaranteed Robustness

Frontier AI systems increasingly train on specialized expert data, from clinical records to proprietary research to curated datasets, yet current training pipelines treat all samples identically. A Nobel laureate's contribution receives the same weight as an unverified submission. We introduce ANML (Attribution-Native Machine Learning), a framework that weights training samples by four quality factors: gradient-based consistency (q), verification status (v), contributor reputation (r), and temporal relevance (T). By combining what the model observes (gradient signals) with what the system knows about data provenance (external signals), ANML produces per-contributor quality weights that simultaneously improve model performance and enable downstream attribution. Across 5 datasets (178-32,561 samples), ANML achieves 33-72% error reduction over gradient-only baselines. Quality-weighted training is data-efficient: 20% high-quality data outperforms 100% uniformly weighted data by 47%. A Two-Stage Adaptive gating mechanism guarantees that ANML never underperforms the best available baseline, including under strategic joint attacks combining credential faking with gradient alignment. When per-sample detection fails against subtle corruption, contributor-level attribution provides 1.3-5.3x greater improvement than sample-level methods, with the advantage growing as corruption becomes harder to detect.

cs.LG↗

arXiv2026

Attention Is Not Retention: The Orthogonality Constraint in Infinite-Context Architectures

Biological memory solves a problem that eludes current AI: storing specific episodic facts without corrupting general semantic knowledge. Complementary Learning Systems theory explains this through two subsystems - a fast hippocampal system using sparse, pattern-separated representations for episodes, and a slow neocortical system using distributed representations for statistical regularities. Current AI systems lack this separation, attempting to serve both functions through neural weights alone. We identify the Orthogonality Constraint: reliable memory requires orthogonal keys, but semantic embeddings cannot be orthogonal because training clusters similar concepts together. The result is Semantic Interference (connecting to what cognitive psychologists have long observed in human memory), where neural systems writing facts into shared continuous parameters collapse to near-random accuracy within tens of semantically related facts. Through semantic density (rho), the mean pairwise cosine similarity, we show collapse occurs at N=5 facts (rho > 0.6) or N ~ 20-75 (moderate rho). We validate across modalities: 16,309 Wikipedia facts, scientific measurements (rho = 0.96, 0.02% accuracy at N=10,000), and image embeddings (rho = 0.82, 0.05% at N=2,000). This failure is geometric - no increase in model capacity can overcome interference when keys share semantic overlap. We propose Knowledge Objects (KOs): structured facts with hash-based identity, controlled vocabularies, and explicit version chains. On Wikipedia facts, KO retrieval achieves 45.7% where Modern Hopfield Networks collapse to near-zero; hash-based retrieval maintains 100%. Production systems (Claude Memory, ChatGPT Memory) store unstructured text, causing schema drift (40-70% consistency) and version ambiguity. Knowledge Objects provide the discrete hippocampal component that enables reliable bicameral memory.

q-bio.NC↗