Qorx Reference Papers And External Sources
Qorx is not built from vibes. The current repo keeps a local PaperQA corpus in
research/papers, plus official provider and provenance references in the docs.
This file is the readable map.
The papers support the architecture class around Qorx: retrieval-backed context
omission, prompt compression, repository memory, cache-aware request design,
agent memory, and runtime cache/KV scope. They do not prove that Qorx wins
on every task. Qorx-specific claims still need Qorx-specific benchmarks.
Prompt Compression
| Reference |
Local file or source |
How it relates to Qorx |
| LLMLingua |
research/papers/llmlingua_2310.05736.pdf |
Learned prompt compression supports the broader idea that prompts can be shortened while preserving useful information. Qorx core does not bundle LLMLingua. |
| LongLLMLingua |
research/papers/longllmlingua_2310.06839.pdf |
Long-context prompt compression and budget control. Qorx uses deterministic budgeted packing and extractive squeeze in the portable core. |
| Active Context Compression |
research/papers/active_context_compression_2601.07190.pdf |
Active context pruning supports the idea that not all context should remain visible all the time. |
| Gist Tokens |
research/papers/gist_tokens_2304.08467.pdf |
Soft-token memory is a model-side technique. Qorx treats this as adapter/future scope because vendor CLIs cannot consume arbitrary learned gist tokens. |
| Experience Compression Spectrum |
research/papers/experience_compression_spectrum_2604.15877.pdf |
Useful for thinking about compression levels and provenance loss. Qorx keeps exact local fallback instead of relying only on summaries. |
Repository And Code Context
| Reference |
Local file or source |
How it relates to Qorx |
| ReACC |
research/papers/reacc.pdf / ACL 2022 source |
Retrieval-augmented code completion supports bringing related code into the model context. Qorx supplies local code quarks to downstream agents. |
| Codebase-Memory |
research/papers/codebase_memory_2603.27277.pdf |
Codebase memory and graph-style context motivate Qorx’s lightweight symbol and relation surfaces. |
| BM25 and lexical retrieval |
referenced in the evidence map |
Exact lexical retrieval remains a strong baseline. Qorx uses deterministic sparse terms plus path and symbol boosts. |
| Aider repository map |
official aider docs |
Repository maps are useful, but Qorx keeps a local quark store and budgeted evidence routes rather than only a static map. |
Agent Memory And Long-Horizon Context
| Reference |
Local file or source |
How it relates to Qorx |
| AgeMem |
research/papers/agemem_2601.01885.pdf |
Explicit memory operations matter for agents. Qorx exposes local memory CRUD and summaries. |
| AtomMem |
research/papers/atommem_2601.08323.pdf |
Atomized memory matches Qorx’s quark-level evidence approach, but Qorx keeps its implementation deterministic. |
| Titans |
research/papers/titans_2501.00663.pdf |
Neural long-term memory is a real research direction. Qorx does not claim a Titans-like learned memory runtime. |
| TokMem |
research/papers/tokmem_2510.00444.pdf |
Token memory research informs future adapter ideas, not the current portable-core claim. |
| Memory Survey |
research/papers/memory_survey_2603.07670.pdf |
Broad survey background for explicit memory design. |
| Structural Memory |
research/papers/structural_memory_2412.15266.pdf |
Supports structured memory and provenance-aware state. |
Hierarchical Memory
| Reference |
Local file or source |
How it relates to Qorx |
| H-MEM |
research/papers/hmem_2507.22925.pdf |
Hierarchical memory supports multi-layer retrieval. Qorx implements deterministic lattice layers. |
| HiMem |
research/papers/himem_2601.06377.pdf |
Long-horizon memory organization. Qorx uses local mementos and raw-quark provenance. |
| TierMem |
research/papers/tiermem_2602.17913.pdf |
Provenance-aware tiered memory is close to Qorx’s lattice and attestation model. |
| GAM |
research/papers/gam_2604.12285.pdf |
Graph-based agentic memory supports the idea of relations across memory nodes. Qorx keeps graph work lightweight in core. |
Cache And Reuse
| Reference |
Local file or source |
How it relates to Qorx |
| Preble |
research/papers/preble_2407.00023.pdf |
Prefix/cache-aware request design. Qorx has cache-plan for stable prefix and dynamic tail separation. |
| Don’t Break the Cache |
research/papers/dont_break_cache_2601.06007.pdf |
Supports careful prompt structure so provider caches remain useful. |
| Similarity Caching |
research/papers/similarity_caching_1912.03888.pdf |
Approximate reuse can save cost but has correctness tradeoffs. Qorx keeps approximate answer replay out of the default path. |
| GPT Semantic Cache |
research/papers/gpt_semantic_cache_2411.05276.pdf |
Semantic caching supports future guarded adapters. Qorx ships exact replay cache first. |
| RAGCache |
research/papers/ragcache_2404.12457.pdf |
Retrieval cache design for RAG workflows. |
| Cache-Craft |
research/papers/cache_craft_2502.15734.pdf |
Chunk cache management for RAG. |
| Approximate Caching for RAG |
research/papers/approximate_caching_rag_2503.05530.pdf |
Approximate reuse is useful but must be measured and guarded. |
| Domain-Specific Semantic Cache |
research/papers/domain_specific_semantic_cache_2504.02268.pdf |
Domain-specific embeddings can improve cache reuse, but Qorx core avoids mandatory embedding runtimes. |
| vCache |
research/papers/vcache_2502.03771.pdf |
Verified semantic prompt caching supports the idea of cache correctness checks. |
| ContextPilot |
research/papers/contextpilot_2511.03475.pdf |
Long-context reuse. Qorx handles reuse through local handles and evidence resolution. |
| QVCache |
research/papers/qvcache_2602.02057.pdf |
Query-aware vector cache ideas inform future cache adapters. |
Runtime And KV Scope
| Reference |
Local file or source |
How it relates to Qorx |
| TurboQuant |
research/papers/turboquant_2504.19874.pdf |
KV/cache compression is a runtime measurement problem. Qorx can emit hints but does not claim realized TurboQuant/vLLM gains without a runtime proof. |
| vCache and QVCache |
local cache papers above |
Useful for guarded cache reuse and query-aware cache design. |
| Source |
URL |
Qorx note |
| OpenAI prompt caching |
https://platform.openai.com/docs/guides/prompt-caching |
Provider-side cache behavior is separate from Qorx local context omission. |
| Anthropic prompt caching |
https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching |
Qorx can help structure stable prefixes, but provider cache hits must be measured upstream. |
| Gemini context caching |
https://ai.google.dev/gemini-api/docs/caching/ |
Same provider-cache measurement note. |
| Claude Code memory |
https://docs.anthropic.com/en/docs/claude-code/memory |
Memory files are useful, but Qorx adds a live local resolver/index path. |
Gemini CLI GEMINI.md |
https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/gemini-md.md |
Context files are not the same as budgeted local evidence retrieval. |
| Cursor codebase indexing |
https://docs.cursor.com/context/codebase-indexing |
Cursor’s server-backed indexing is a different deployment model. Qorx keeps the core local. |
| Cursor secure codebase indexing |
https://cursor.com/blog/secure-codebase-indexing |
Useful comparison for privacy and indexing models. |
Provenance, Signatures, And Storage
| Source |
URL |
Qorx note |
| Protocol Buffers |
https://protobuf.dev/ |
Qorx uses protobuf-envelope persisted state and a typed context snapshot. |
| NIST FIPS 204 |
https://csrc.nist.gov/pubs/fips/204/final |
Qorx hybrid attestation uses post-quantum signature practice as a reference point. |
| C2PA Specification |
https://spec.c2pa.org/specifications/specifications/2.4/specs/C2PA_Specification.html |
Qorx provenance is local metadata, not a full embedded media manifest. |
| Microsoft kernel-mode signing requirements |
https://learn.microsoft.com/en-us/windows-hardware/drivers/install/kernel-mode-code-signing-requirements–windows-vista-and-later- |
Real RAM-drive drivers have OS/runtime requirements. Qorx reports RAM mode separately from portable disk-backed mode. |
PaperQA Result Scope
PaperQA has been used here as a research audit path, not as an oracle. The local
corpus supports the architecture class. It does not by itself prove Qorx-specific
accuracy, latency, cost, or task-success improvement.
The next benchmark that matters is empirical: multiple repositories, repeated
agent tasks, routed provider traffic, retrieval-support scoring, latency, cache
hit rates, and invoice/provider-token comparison.