Alignment Research

Geometric and structural methods for AI alignment. Three research threads, documented with reproducible experiments and concrete results.

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Research Threads

1. Hodge Preference Geometry

Directory: hodge_preference_geometry/

Using combinatorial Hodge theory to decompose LLM preference feedback into transitive (trustworthy) and cyclic (inconsistent) components — and building a Riemannian safety geometry that makes dangerous policy regions geometrically unreachable rather than merely penalized.

Key results:

• Hodge-filtered reward signal (gradient component mean: 0.273) vs. unfiltered baseline (0.813) — a 3× reduction in cyclic noise entering training
• First cohomology H¹ score of 2.47 on the HH-RLHF dataset, quantifying the degree of preference inconsistency
• Conformal safety barriers validated against adversarial jailbreak trajectories — policies trained with the conformal metric stay within the safe manifold; CPO-trained baselines escape
• Sandbagging v2: policy robustness experiment across 4 seeds × 6 training checkpoints, tracking metric-field evolution

Core modules:

File	Role
discrete_hodge_rank.py	Helmholtz-Hodge decomposition on preference graphs
conformal_safety.py	Conformal metric g_ij = e^{2σ}δ_ij creating infinite barriers
enhanced_sgpo.py	Sheaf-Geodesic Policy Optimizer composing both modules

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2. Ontological Embeddings

Directory: ontological_embeddings/ Paper: Interpretable Knowledge Graph Reasoning via Sheaf Cohomology (arXiv, cs.LG)

Bridging symbolic and statistical AI using Ologs (category-theoretic knowledge representations). Core claim: transformer attention implicitly implements categorical semantics, and making that structure explicit — via proof objects and sheaf cohomology — reduces hallucination and makes reasoning auditable.

Key results:

• HDC/Sheaf pipeline: MRR 0.346, Hits@1 0.242, Hits@10 0.524 on FB15K-237 (competitive with ConvE ~0.325, RotatE ~0.338)
• Conflict detection: H¹ cohomology increases by +53 when 76 conflicts injected into a clean graph (base H¹ = 5 → 58), validating sheaf-theoretic inconsistency detection
• WN18RR consistency score 0.633 vs FB15K-237 0.292, correctly reflecting WordNet's tree-structured ontology vs. Freebase's multi-relational web
• Attention ablation v2: ontological head parameterization improves factual consistency across 3 benchmark datasets

Core modules:

File	Role
olog_core.py	Category-theoretic knowledge graph: types, morphisms, commutativity
ghrr_encoder.py	Hyperdimensional (HDC) encoder with non-commutative relation binding
ontology_sheaf.py	Cellular sheaf over an Olog; H⁰/H¹ cohomology for inconsistency detection
ontological_attention.py	Attention heads gated by Olog reachability (the (B) locus)
proof_objects.py	Formal proof objects for logical verification
proof_guided_generation.py	Prove-then-generate pipeline with the constrained decoder (the (D) locus)
hdc_sheaf_pipeline.py	End-to-end HDC/Sheaf link-prediction and cohomology pipeline
baseline_benchmarks.py	TransE / RotatE / DistMult / ComplEx baselines on FB15K-237, WN18RR

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3. Peer-Consistency Geometry

Directory: peer_consistency_geometry/ Writeup: Cross-Model Representational Divergence on Deceptive Responses

Treating a panel of independently-trained instruct LMs as the cover of a cellular sheaf over the space of model outputs, and using the cocycle obstruction ‖δ¹c‖ to detect strategic deception and alignment-faking reasoning. The signal is relative (a comparison of conceptual geometry across models, not an absolute deception verdict) and emerges with model capacity.

Key results (7–9B panel: Yi-1.5-9B-Chat, Zephyr-7B-beta, Qwen2.5-7B-Instruct, length-matched, 5 split-seeds × 4 subsample seeds):

• Persuasion deception (LIARS'-BENCH convincing-game): AUC 0.661 ± 0.011, p=2.5e−6
• Agentic/strategic deception (LIARS'-BENCH insider-trading): AUC 0.625 ± 0.012, p=8e−8
• Alignment-faking reasoning (Anthropic alignment-faking-rl): AUC 0.384 ± 0.006, p=5.9e−8 — sign-flipped (faking is more peer-consistent; honest refusal diverges); held at 0.391 ± 0.023 after stripping train/deploy lexical cues
• Selective: weak/null on flat instructed lies (AUC 0.528) and templated refusals
• Where the construct lives: separates actions on action-labeled datasets, reasoning on reasoning-labeled datasets — supports a unified "the panel disagrees on a different manifold for deceptive vs honest content" reading

Core modules:

File	Role
src/peer_sheaf.py	Affine restriction maps (ridge), per-pair residuals, lossiness L(x)
src/peer_hodge.py	PeerComplex, δ⁰ + δ¹ coboundaries, full-rank and low-rank Hodge decompositions
modal/embed_panel.py	Modal A100 runner (HH-RLHF + LIARS'-BENCH text-driven embeds)
experiments/E6_7B_panel.py	SVD-free cocycle_blockwise analysis; --texts-json length-match

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4. TLTS-Compilation

Directory: tlts_compilation/ Paper: TLTS-Compilation: A Neurosymbolic Framework for Type-Safe and Verifiable Transformers (NeSy 2026 submission, double-blind)

A neurosymbolic framework that unifies two recent threads — type-safe (ontology-gated) attention and program-compiled transformers — as one construction: compile a typed labeled transition system (TLTS) into a transformer. The framework names three inference-time loci where the domain rule can be enforced (in-FFN gates, pre-decoder logit masks, post-hoc audit), and ships a JSON certificate format that a third party can re-check without model weights.

Key results (synthetic harness, 7-type e-commerce Olog, N=1000 trajectories):

• Pre-decoder masking (D) and FFN-hybrid (C) achieve 100% soundness under both well-aligned and adversarially misaligned priors; unconstrained baseline (A) collapses to 4.2% under a misaligned prior
• Non-obvious finding: attention-layer reachability masking alone is insufficient — (B′) variant scores 4.3% / 61.5% (BAD/GOOD prior), barely above (A). The decoder must also enforce direct-edge admissibility, not just reachability to the destination type
• Latency: (C) wins by ~30% over (D) when the functional fragment of the Olog is large (deterministic forward steps skip sampling); crossover at fn-ratio ≈ 0.2
• Audit certificates (JSON) catch all tampered traces in the demo; verifier needs only the TLTS spec, no model weights or PyTorch

Core artifacts:

File	Role
supplementary/experiment_loci_comparison.py	The four-locus framework's headline soundness/fluency numbers
supplementary/experiment_real_attention_b.py	Production-attention mask audit (66.7% of mass on reachable-but-not-δ pairs)
supplementary/experiment_topology_sweep.py	Functional-fragment ablation; deployment heuristic for (C)/(D) choice
supplementary/verification_certificate.py	Emit + verify the audit certificate
supplementary/sample_audit_certificate.json	Reference certificate format

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Writing

writing/ contains six articles explaining the work for a general technical audience. These were written to accompany the research, not summarize it after the fact.

Article	Subject
01 — Why Your LLM Hallucinates	Category theory as the missing type system for language generation
02 — Attention, But Make It Type-Safe	Ontological constraints in transformer attention
03 — From Proofs to Text	Curry-Howard correspondence extended to NLG
04 — Building an Auditable AI	Full walkthrough: ontology to deployment
05 — Compiling Programs Into Attention	TLTS-compilation as the procedural cousin of type-safe attention
Stigmergy and the Architecture of Autonomy	Decentralized multi-agent coordination via environmental signals

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Reproducing Results

Both threads have been run on Modal A100 GPUs. Local reproduction on CPU is possible for the analysis scripts; training requires GPU.

# Clone and set up
git clone https://github.com/oasis-main/alignment_research
cd alignment_research
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt   # coming soon

# Reproduce Hodge decomposition
cd hodge_preference_geometry
python discrete_hodge_rank.py     # generates decomposition, prints H¹ score

# Reproduce the Olog thread
cd ../ontological_embeddings
python olog_core.py               # builds graph, runs sheaf cohomology
python hdc_sheaf_pipeline.py      # HDC/Sheaf link prediction + H¹ conflict detection
python attention_ablation_experiment.py --epochs 300 --embed-dim 64 --lr 0.003   # typed-attention ablation
python baseline_benchmarks.py     # TransE / RotatE / etc. on WN18RR

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Related Work

See METHODS.md for the mathematical foundations and citations.

Venue targets: ICML 2026 (Hodge thread); NeurIPS 2026 Workshops — SafeML / ATTRIB / SoLaR — (Peer-consistency thread). The Olog thread is being posted to arXiv (cs.LG) — see ontological_embeddings/paper/.