Combine recent validated techniques into a productizable artifact.

.ares_cache_disk.bin, .cache_shelf, .gitignore, .invention_cache, cache_data, cache_storage.db, demo_cache.bin, demo_cache_db

SSM State Recycling for Tooling -> Success (score=2.20). Promote this line toward an invention brief.

.local_states.json, DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, ssm_state_recycling_for_tooling/__init__.py, ssm_state_recycling_for_tooling/agent.py, ssm_state_recycling_for_tooling/agent_context.py

Combine recent validated techniques into a productizable artifact.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, frequency_modulated_state_spaces_fmss/__init__.py, frequency_modulated_state_spaces_fmss/_state_space.py, frequency_modulated_state_spaces_fmss/agent.py, frequency_modulated_state_spaces_fmss/analysis.py

Combine recent validated techniques into a productizable artifact.

cache.json, DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, delta_state_compression_for_long_context/__init__.py, delta_state_compression_for_long_context/algorithm.py, delta_state_compression_for_long_context/analysis.py

Tiered Precision State Cache (TPSC) -> Success (score=6.58). Promote this line toward an invention brief.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, dist/tiered_precision_state_cache_tpsc-0.1.0.tar.gz, tiered_precision_state_cache_tpsc/__init__.py, tiered_precision_state_cache_tpsc/_cache.py, tiered_precision_state_cache_tpsc/analysis.py

Combine recent validated techniques into a productizable artifact.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, dynamic_precision_state_skipping/__init__.py, dynamic_precision_state_skipping/adjuster.py, dynamic_precision_state_skipping/adjustment.py, dynamic_precision_state_skipping/analyzer.py

Research brief and prototype implementation of Closed-form Continuous-time (CFC) Neural Networks integrated with ARES tiered state caching techniques.

Context-aware multi-model orchestration layer that routes LLM requests between distilled and generalist backends for lower latency and VRAM usage.

__init__.py, README.md, requirements.txt, ares_mesh/__init__.py, ares_mesh/config.py, ares_mesh/demo.py, ares_mesh/models.py, ares_mesh/orchestrator.py

Asynchronous Host-Device State Ring Buffer -> Success (score=6.33). Promote this line toward an invention brief.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, asynchronous_host_device_state_ring_buffer/__init__.py, asynchronous_host_device_state_ring_buffer/core.py, asynchronous_host_device_state_ring_buffer/demo.py, asynchronous_host_device_state_ring_buffer/__pycache__/__init__.cpython-314.pyc

A pure-Python implementation of Cross-Layer State Distillation, a technique where internal activations (states) of a teacher layer are used to train a student layer within the same or a different network. This project implements a lightweight neural engine from scratch to demonstrate how knowledge can be compressed from deeper representations into shallower layers.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, cross_layer_state_distillation_clsd/__init__.py, cross_layer_state_distillation_clsd/core.py, cross_layer_state_distillation_clsd/__pycache__/__init__.cpython-314.pyc, cross_layer_state_distillation_clsd/__pycache__/core.cpython-314.pyc

Dormant State Offloading (DSO) -> Success (score=5.58). Promote this line toward an invention brief.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, dormant_state_offloading_dso/__init__.py, dormant_state_offloading_dso/core.py, dormant_state_offloading_dso/demo.py, dormant_state_offloading_dso/__pycache__/__init__.cpython-314.pyc

A Python implementation of a state management system that dynamically adjusts quantization precision based on the information entropy of the data stream. It reduces precision for low-entropy (redundant) states to save resources and increases precision for high-entropy (complex) states to maintain fidelity.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, entropy_adaptive_state_quantization/__init__.py, entropy_adaptive_state_quantization/core.py, entropy_adaptive_state_quantization/utils.py, entropy_adaptive_state_quantization/__pycache__/__init__.cpython-314.pyc

A lightweight, local-first memory management library that applies exponential decay to stored information over time. The decay rate is dynamically modified (gated) based on the relevance of keywords in the user's prompt, ensuring that salient information persists while irrelevant noise fades away.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, prompt_gated_temporal_decay_pgtd/__init__.py, prompt_gated_temporal_decay_pgtd/core.py, prompt_gated_temporal_decay_pgtd/__pycache__/__init__.cpython-314.pyc, prompt_gated_temporal_decay_pgtd/__pycache__/core.cpython-314.pyc

A lightweight, local Python implementation of a sparse associative memory system. It models state transitions as a directed graph where memory is stored sparsely (only existing connections are tracked). The system allows for associative retrieval (spreading activation) and sparse pruning to remove weak associations, optimizing for memory and relevance.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, sparse_associative_state_sas/__init__.py, sparse_associative_state_sas/core.py, sparse_associative_state_sas/__pycache__/__init__.cpython-314.pyc, sparse_associative_state_sas/__pycache__/core.cpython-314.pyc

A lightweight, dependency-free Python implementation of a sparse state machine. SASI allows for the injection of high-dimensional sparse vectors into a persistent state context, utilizing associative memory retrieval to update state based on similarity rather than direct address.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, sparse_associative_state_injection_sasi/__init__.py, sparse_associative_state_injection_sasi/core.py, sparse_associative_state_injection_sasi/__pycache__/__init__.cpython-314.pyc, sparse_associative_state_injection_sasi/__pycache__/core.cpython-314.pyc

Zero-Copy Memory-Mapped State Streaming -> Success (score=6.33). Promote this line toward an invention brief.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, zero_copy_memory_mapped_state_streaming/__init__.py, zero_copy_memory_mapped_state_streaming/core.py, zero_copy_memory_mapped_state_streaming/__pycache__/__init__.cpython-314.pyc, zero_copy_memory_mapped_state_streaming/__pycache__/core.cpython-314.pyc

Combine recent validated techniques into a productizable artifact.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, adaptive_tool_state_quantization/__init__.py, adaptive_tool_state_quantization/_dynamic_precision.py, adaptive_tool_state_quantization/adapter.py, adaptive_tool_state_quantization/adaptive_agent.py

Comprehensive research inventories for six alternative AI/ML paradigms not currently covered by ARES: Hyperdimensional Computing (HDC), Vector Symbolic Architectures (VSA), Sparse Distributed Representations (SDR), Alternatives to Backpropagation, Compositional Generalization, and Localist Representations. Includes detailed research briefs, backfill seed lists for 2023-2026 papers, structured knowledge entries, and an integrated synthesis identifying high-priority research gaps and quick-win opportunities.

MANIFEST.md, PHASE_1_2_SUMMARY.md, README.md, RESEARCH_SYNTHESIS.md, knowledge/ares_knowledge_update.json, knowledge/phase1_knowledge_ingestion.py, knowledge/phase2_backfill_seeds.py, knowledge/research_questions_gaps.json

A production-ready, drop-in compression library for LLM agent orchestrators. Provides OpenAI-compatible, Transformers-compatible, and string adapters that accept prompts/messages and return compressed outputs ready for LLM consumption. Uses semantic importance scoring, not mechanical sampling.

pyproject.toml, README.md, run_demo.py, run_tests.py, VERIFICATION_SUMMARY.md, .pytest_cache/.gitignore, .pytest_cache/CACHEDIR.TAG, .pytest_cache/README.md

A drop-in compression library for LLM orchestrators. Provides OpenAI-compatible, Transformers-compatible, and string adapters that accept prompts/messages and return compressed outputs ready for LLM consumption. Not a tensor-level helper - this operates at the message/text level with real adapter surfaces.

.gitignore, ANALYSIS.md, API_REFERENCE.md, fix.md, pyproject.toml, README.md, REHARDENING_COMPLETE.md, REHARDENING_SUMMARY.md

A drop-in compression library for LLM orchestrators. Provides OpenAI-compatible, Transformers-compatible, and string adapters that accept prompts/messages and return compressed outputs ready for LLM consumption. Not a tensor-level helper - this operates at the message/text level with real adapter surfaces.

pyproject.toml, README.md, run_demo.py, .pytest_cache/.gitignore, .pytest_cache/CACHEDIR.TAG, .pytest_cache/README.md, .pytest_cache/v/cache/lastfailed, .pytest_cache/v/cache/nodeids

A Python implementation of a tiered caching system for State Space Model (SSM) states, designed to simulate offloading older states to system RAM (FP16) while keeping active states in GPU VRAM (FP8). This approach aims to enable effectively infinite context windows on consumer hardware by managing memory hierarchies explicitly.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, tiered_ssm_state_cache/__init__.py, tiered_ssm_state_cache/cache.py, tiered_ssm_state_cache/__pycache__/__init__.cpython-311.pyc, tiered_ssm_state_cache/__pycache__/cache.cpython-311.pyc

A plug-and-play context compression layer for LLM orchestration stacks. Features semantic token pruning, dynamic precision switching, entropy-gated processing, and tiered caching to reduce memory usage by 40-60% with minimal accuracy loss.

compression_layer_standalone.py, LICENSE, ORCHESTRATION.md, plug_and_play.py, pyproject.toml, QUICKSTART.md, README.md, run_demo.py

Production-build FastAPI starter scaffold generated by ARES with a strict developer contract, installable package artifacts, and clean-room release verification.

.gitignore, clean_and_build.py, fix_config.py, MANIFEST.in, pyproject.toml, pyproject_fixed.toml, README.md, run_demo.py

A comprehensive, modular RAG optimization framework combining semantic grounding, two-stage distillation, adaptive compression, and throughput optimization for production-ready retrieval-augmented generation.

.gitignore, .pre-commit-config.yaml, CHANGELOG.md, CONTRIBUTING.md, DESIGN_BRIEF.md, LICENSE, pyproject.toml, pytest.ini

No summary available yet.

Local-first FastAPI starter scaffold generated by ARES with a strict developer contract, README, and structural verification.

.gitignore, pyproject.toml, README.md, run_demo.py, app/__init__.py, app/main.py, app/api/__init__.py, app/api/routes.py

An integrated system combining all 12 ARES inventions into a single, coherent state management architecture for SSM-based language models. Features entropy-gated processing, tiered storage hierarchy, spectral compression, and gradient-optimized training.

ARCHITECTURE.md, DESIGN_BRIEF.md, LICENSE, pyproject.toml, README.md, run_demo.py, ares_unified_state/__init__.py, ares_unified_state/compression.py

High entropy tokens carry more information and require higher state fidelity. Low entropy tokens (tokens, stop words) can be processed with 4-bit states. This dynamic switching will reduce average memory bandwidth by 25%.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, entropy_gated_state_speculative_decoding/__init__.py, entropy_gated_state_speculative_decoding/ssm_core.py, entropy_gated_state_speculative_decoding/__pycache__/__init__.cpython-311.pyc, entropy_gated_state_speculative_decoding/__pycache__/__init__.cpython-313.pyc

Gradient-Scoped State Unrolling (GSSU) -> Success (score=6.32). Promote this line toward an invention brief.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, gradient_scoped_state_unrolling_gssu/__init__.py, gradient_scoped_state_unrolling_gssu/core.py, gradient_scoped_state_unrolling_gssu/demo.py, gradient_scoped_state_unrolling_gssu/ssm_cell.py

Asynchronous State Offloading (ASO) -> Success (score=6.58). Promote this line toward an invention brief.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, asynchronous_state_offloading_aso/__init__.py, asynchronous_state_offloading_aso/core.py, asynchronous_state_offloading_aso/demo.py, asynchronous_state_offloading_aso/__pycache__/__init__.cpython-311.pyc

Entropy-Gated Dynamic Precision (EGDP) -> Success (score=6.33). Promote this line toward an invention brief.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, entropy_gated_dynamic_precision_egdp/__init__.py, entropy_gated_dynamic_precision_egdp/core.py, entropy_gated_dynamic_precision_egdp/demo.py, entropy_gated_dynamic_precision_egdp/__pycache__/__init__.cpython-311.pyc

Gated State Quantization (GSQ) -> Success (score=6.58). Promote this line toward an invention brief.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, gated_state_quantization_gsq/__init__.py, gated_state_quantization_gsq/gsq_core.py, gated_state_quantization_gsq/__pycache__/__init__.cpython-311.pyc, gated_state_quantization_gsq/__pycache__/gsq_core.cpython-311.pyc

Low-Rank Associative State Injection (LASI) -> Success (score=6.58). Promote this line toward an invention brief.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, low_rank_associative_state_injection_lasi/__init__.py, low_rank_associative_state_injection_lasi/core.py, low_rank_associative_state_injection_lasi/demo.py, low_rank_associative_state_injection_lasi/__pycache__/__init__.cpython-311.pyc

Spectral State Cache (SSC) -> Success (score=6.42). Promote this line toward an invention brief.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, spectral_state_cache_ssc/__init__.py, spectral_state_cache_ssc/core.py, spectral_state_cache_ssc/transform.py, spectral_state_cache_ssc/__pycache__/__init__.cpython-311.pyc

Student hypothesis: dynamic_precision + ssm_mamba co-design -> Success (score=6.58). Promote this line toward an invention brief.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, student_hypothesis_dynamic_precision_ssm_mamba_co_design/__init__.py, student_hypothesis_dynamic_precision_ssm_mamba_co_design/core.py, student_hypothesis_dynamic_precision_ssm_mamba_co_design/demo.py, student_hypothesis_dynamic_precision_ssm_mamba_co_design/__pycache__/__init__.cpython-311.pyc

Student hypothesis: ssm + cache co-design -> Success (score=6.33). Promote this line toward an invention brief.

DESIGN_BRIEF.md, pyproject.toml, README.md, run_demo.py, student_hypothesis_ssm_cache_co_design/__init__.py, student_hypothesis_ssm_cache_co_design/core.py, student_hypothesis_ssm_cache_co_design/__pycache__/__init__.cpython-311.pyc, student_hypothesis_ssm_cache_co_design/__pycache__/core.cpython-311.pyc

By asynchronously offloading 'stale' SSM states to CPU RAM using INT4 quantization (EGDP) and keeping the immediate state in FP16, we can maintain throughput while exceeding GPU memory limits.

DESIGN_BRIEF.md, README.md